ACL 2025 Papers — Page 12
Annual Meeting of the Association for Computational Linguistics · 1699 papers
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer
Guodong Du (Harbin Institute of Technology), Min Zhang (Harbin Institute of Technology)
CompressionKnowledge DistillationTransformerLarge Language ModelSupervised Fine-TuningImageTextMultimodality
🎯 What it does: This paper proposes Neural Parameter Search (NPS), a gradient-free pruning strategy based on subspaces of task vectors, which efficiently prunes fine-tuned models while retaining original pre-trained parameters, achieving knowledge transfer, model fusion, and compression.
Neural Topic Modeling with Large Language Models in the Loop
Xiaohao Yang (Monash University), Lan Du (Monash University)
Explainability and InterpretabilityKnowledge DistillationRepresentation LearningTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
🎯 What it does: Proposes the LLM-ITL framework, integrating large language models (LLM) with neural topic models (NTM). The LLM refines the topic words learned by NTM and aligns them using Optimal Transport, enhancing the interpretability of topics;
Neuron Empirical Gradient: Discovering and Quantifying Neurons’ Global Linear Controllability
Xin Zhao (University of Tokyo), Naoki Yoshinaga (University of Tokyo)
Explainability and InterpretabilityTransformerTextBenchmark
🎯 What it does: By intervening in the feedforward layer neurons of pre-trained language models (PLM), it is found that the activation and changes in output probability exhibit a linear relationship, which is quantified as the neuron empirical gradient (NEG).
Neuron-Level Sequential Editing for Large Language Models
Houcheng Jiang (University of Science and Technology of China), Xiang Wang (University of Science and Technology of China)
Representation LearningTransformerLarge Language ModelText
🎯 What it does: Proposed a neuron-level based sequential editing method (NSE) for continuously modifying the internal knowledge of a model without retraining large language models.
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
Ruisheng Cao (Shanghai Jiao Tong University), Kai Yu (Shanghai Jiao Tong University)
RetrievalTransformerLarge Language ModelAgentic AIVision Language ModelTextRetrieval-Augmented Generation
🎯 What it does: Propose NeuSym-RAG, an interactive PDF question-answering framework that combines vector retrieval with SQL symbolic retrieval.
NewsInterview: a Dataset and a Playground to Evaluate LLMs’ Grounding Gap via Informational Interviews
Alexander Spangher (University of Southern California), Jonathan May (University of Southern California)
TransformerLarge Language ModelPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Collected and cleaned approximately 40,000 dual-person news interview records from NPR and CNN, conducted discourse analysis, and constructed the NewsInterview simulation environment to evaluate the grounding and persuasion capabilities of LLMs in information-seeking dialogues.
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization
Hyuntak Kim (CJ Corporation), Byung-Hak Kim (CJ Corporation)
GenerationCompressionTransformerLarge Language ModelAgentic AIText
🎯 What it does: Proposed a multi-agent LLM framework called NEXUSSUM for structured compression to generate summaries of long narrative texts.
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
Zheyuan Zhang (University of Notre Dame), Yanfang Ye (University of Notre Dame)
Graph Neural NetworkTransformerLarge Language ModelGraphTabularBiomedical DataBenchmarkRetrieval-Augmented Generation
🎯 What it does: Proposed the NGQA benchmark, leveraging knowledge graphs to evaluate personalized nutrition and health question answering;
No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions
Neha Srikanth (University of Maryland), Jordan Lee Boyd-Graber
Explainability and InterpretabilityData-Centric LearningTransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: This paper constructs a computational framework based on inquiry semantics, utilizing question-answer interactions collected from Reddit r/NoStupidQuestions to generate multiple interpretations of questions and statistically analyze the distribution of interpretations chosen by respondents. It then quantifies the 'difficulty of answering' through entropy values and compares the behavior of interpretation distributions with 12 instruction-tuned large language models.
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attributions Explainability
Joakim Edin (Corti), Maria Maistro (University of Copenhagen)
Explainability and InterpretabilityTransformerText
🎯 What it does: Proposed and evaluated Normalized AOPC (NAOPC), addressing the bias in cross-model comparisons of traditional AOPC by calculating the lower/upper bounds of AOPC for each model and input, followed by min-max normalization;
Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering
Xinran Chen (University of Chinese Academy of Sciences), Le Sun (Chinese Academy of Sciences)
RetrievalTransformerLarge Language ModelTextRetrieval-Augmented Generation
🎯 What it does: Propose a recall-oriented adaptive learning method called ReAL, which dynamically adjusts term weights in PLM-assisted query expansion to improve the recall rate and final QA performance of sparse retrieval in open-domain QA.
Nudging: Inference-time Alignment of LLMs via Guided Decoding
Yu Fei (University of California Irvine), Sameer Singh (University of California Irvine)
TransformerLarge Language ModelText
🎯 What it does: Propose a no-training method called NUDGING for aligning any large language model during inference, which improves the output of large models by inserting guiding tokens into the generation process using a small alignment model
NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts
Muhammad Farid Adilazuarda (MBZUAI), Alham Fikri Aji (MBZUAI)
ClassificationRecognitionImage TranslationSegmentationLarge Language ModelVision Language ModelMultimodalityBenchmark
🎯 What it does: Constructed the NUSAAKSARA benchmark dataset, covering 8 indigenous scripts of Indonesia (including Lampung, which lacks Unicode support), and defined multimodal tasks such as image segmentation, OCR, transcription, translation, and language identification;
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
Geliang Ouyang (Huazhong University of Science and Technology), Dongping Chen (Huazhong University of Science and Technology)
AI Code AssistantTransformerLarge Language ModelAgentic AITabularChain-of-Thought
🎯 What it does: Proposes a collaborative agent workflow named NVAGENT for converting natural language queries into visualizations of multi-table data;
OASIS: Order-Augmented Strategy for Improved Code Search
Zuchen Gao (Hong Kong Polytechnic University), Jing Li (Hong Kong Polytechnic University)
RetrievalLarge Language ModelContrastive LearningTextMultimodalityGraph
🎯 What it does: Propose the OASIS framework, which trains code embedding models using sequentially enhanced similarity labels to improve code retrieval performance.
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks
Yu Lin (ByteDance), Sheng Zhong (ByteDance)
ClassificationGenerationSafty and PrivacyTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Proposed a privacy-preserving MLaaS framework called ObfusLM, achieving privacy protection for input and output texts in classification and generation tasks by performing a round of obfuscation on word embeddings and model heads at the model layer;
Odysseus Navigates the Sirens’ Song: Dynamic Focus Decoding for Factual and Diverse Open-Ended Text Generation
Wen Luo (Peking University), Houfeng Wang (Peking University)
GenerationTransformerLarge Language ModelText
🎯 What it does: Designed and implemented a Dynamic Focus Decoding (DFD) method that adaptively adjusts temperature during inference based on KL distribution differences between Transformer layers, enhancing the diversity of generated text while maintaining factual accuracy.
OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval
Wei Yang (Microsoft Research Asia), Jiang Bian (Microsoft Research Asia)
RetrievalComputational EfficiencyTransformerVision Language ModelContrastive LearningMultimodalityRetrieval-Augmented Generation
🎯 What it does: Built a multi-step, coarse-to-fine hierarchical multimodal retrieval + RAG framework to address knowledge-driven visual question answering (KB-VQA) problems.
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference
Xiangyu Zhao (Shanghai Jiao Tong University), Kai Chen (Shanghai AI Laboratory)
TransformerLarge Language ModelSupervised Fine-TuningVision Language ModelImageTextMultimodalityBenchmark
🎯 What it does: Constructed and publicly released the OmniAlign-V multimodal alignment dataset, combined with OpenAI GPT-4o to generate open-ended QA and creative, reasoning tasks, and used this dataset for SFT and DPO training of multimodal large language models (MLLMs), while proposing MM-AlignBench, a high-quality human-annotated alignment benchmark for evaluation.
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction
Haonan Zhang (Tongji University), Yongbin Li (Tongyi Laboratory)
GenerationData SynthesisTransformerLarge Language ModelSupervised Fine-TuningFlow-based ModelTextMultimodalityAudio
🎯 What it does: Developed a role-playing agent (OmniCharacter) capable of achieving seamless speech-text persona interaction, and constructed the OmniCharacter-10K dataset containing 20 roles, 10,072 rounds of multi-turn dialogues, and 135,000 voice responses.
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation
Qinglin Zhang (Tongyi Lab), ShiLiang Zhang
GenerationData SynthesisTransformerLarge Language ModelSupervised Fine-TuningTextMultimodalityAudio
🎯 What it does: Propose OmniFlatten, a GPT-based end-to-end full-duplex speech dialogue model that enables simultaneous bidirectional speech interaction.
On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures
Minh Duc Bui (Johannes Gutenberg University Mainz), Katharina Von Der Wense (Johannes Gutenberg University Mainz)
Explainability and InterpretabilityComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningTextFinance RelatedChain-of-Thought
🎯 What it does: Investigate the fact retrieval capability of large language models (LLMs) across different measurement systems (currency, length, weight), evaluate their default measurement systems, accuracy differences when using non-default systems, and explore the impact of multi-step reasoning (CoT, Seq) on performance and computational cost.
On Many-Shot In-Context Learning for Long-Context Evaluation
Kaijian Zou (University of Michigan), Lu Wang (University of Michigan)
ClassificationTransformerLarge Language ModelPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: This paper systematically evaluates the effectiveness of many-shot in-context learning (ICL) on long context language models (LCLMs), proposes the sample learning ratio (SLR) metric to distinguish between similar sample learning (SSL) and all-sample learning (ASL) tasks, and constructs a comprehensive long context benchmark named MANYICLBENCH covering multiple tasks and models.
On Support Samples of Next Word Prediction
Yuqian Li (East China Normal University), Yuanbin Wu (East China Normal University)
Explainability and InterpretabilityRepresentation LearningData-Centric LearningTransformerLarge Language ModelText
🎯 What it does: This paper conducts data center interpretability analysis on the next-word prediction task of large language models through the representation theorem, identifying supporting samples (with significant impact on prediction parameters) and non-supporting samples, revealing their different roles in model decision-making and representation learning;
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski (KU Leuven), Carolin Lawrence (NEC Laboratories Europe)
Data SynthesisTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
🎯 What it does: Proposed a synthetic data method called SYNQA based on large language models (LLMs) to generate training samples for sentence-level context attribution, and achieved efficient real-time question answering context attribution using a fine-tuned small model.
On Synthetic Data Strategies for Domain-Specific Generative Retrieval
Haoyang Wen (Carnegie Mellon University), Zhiguo Wang (AWS AI)
Data SynthesisRetrievalTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringText
🎯 What it does: Propose a two-stage training framework based on synthetic data to enhance the scalability and performance of domain-specific generative retrieval models.
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models
Catherine Arnett (University of California, San Diego), Ben Bergen
Explainability and InterpretabilityRepresentation LearningTransformerLarge Language ModelTextBenchmark
🎯 What it does: Trained 16 small bilingual GPT-2 models to systematically investigate cross-lingual structural priming effects, examining factors such as language symmetry, language similarity, training dynamics, and catastrophic forgetting.
On the Limit of Language Models as Planning Formalizers
Cassie Huang (Drexel University), Li Zhang (Drexel University)
TransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: This paper systematically evaluates the ability of using large language models (LLMs) as a 'formalizer' to automatically generate complete PDDL (domain and problem files) from natural language descriptions, followed by solving plans through a planner; meanwhile, this method is compared with the approach of directly generating plans using LLM-as-planner.
On the Mutual Influence of Gender and Occupation in LLM Representations
Haozhe An (University of Maryland), Rachel Rudinger (University of Maryland)
Representation LearningLarge Language ModelText
🎯 What it does: Studied the internal representation of name gender in large language models (LLMs) and their mutual influence with occupational bias, systematically analyzing the gender projection of names in different occupational contexts and evaluating their impact on occupational prediction tasks.
On the Relation Between Fine-Tuning, Topological Properties, and Task Performance in Sense-Enhanced Embeddings
Deniz Ekin Yavas (Heinrich Heine University Düsseldorf), Laura Kallmeyer (Université Paris Cité)
Representation LearningTransformerSupervised Fine-TuningContrastive LearningText
🎯 What it does: This study explores the impact of supervised contrastive learning (SCL) and supervised predictive learning (SPL) on the topology of embedding spaces (perceptual alignment, uniformity, isotropy) and downstream semantic distinction tasks (WiC) by enhancing BERT and RoBERTa with semantic knowledge (based on WordNet super-senses).
On the Reliability of Large Language Models for Causal Discovery
Tao Feng (Monash University), Gholamreza Haffari (Monash University)
TransformerLarge Language ModelPrompt EngineeringTextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Investigate the reliability of large language models (LLMs) in causal discovery tasks, exploring the impact of memory, erroneous causal relationships, and context on model performance.
On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs
Herun Wan (School of Computer Science and Technology, Xi'an Jiaotong University, China), Xiang Zhao (National University of Defense Technology)
Anomaly DetectionData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningMixture of ExpertsTextBenchmark
🎯 What it does: Systematically studied the evidence contamination risks of large language models in malicious social text detection, designed 13 LLM-based contamination methods, evaluated their impact on four types of detection tasks, and proposed and experimentally tested three defense strategies (machine-generated text detection, expert mixing, parameter updates).
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit
Weitao Ma (Harbin Institute of Technology), Bing Qin (Harbin Institute of Technology)
Computational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: To address the knowledge update problem in large language models, we propose ONCEEDIT, a method for one-time knowledge editing across multiple models by leveraging a lightweight plugin model and heterogeneous model integration.
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
Ke Yi (South China University of Technology), Jia Li (Hong Kong University of Science and Technology)
Computational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Proposed a Once-for-All Quantization-Aware Fine-Tuning (QFA) framework that can generate multiple low-bitwidth subnetworks in a single training process to meet resource requirements of different deployment scenarios.
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities
Adhiraj Ghosh (University of Tübingen), Matthias Bethge (University of Tübingen)
Benchmark
🎯 What it does: Propose the ONEBench framework, constructing a sustainably growing sample pool and achieving dynamic evaluation of foundational models' open capabilities through sample-level ranking;
Online Iterative Self-Alignment for Radiology Report Generation
Ting Xiao (East China University of Science and Technology), Chenjia Bai (Institute of Artificial Intelligence (TeleAI), China Telecom)
GenerationTransformerSupervised Fine-TuningReinforcement LearningVision Language ModelBiomedical Data
🎯 What it does: Propose an online iterative self-alignment (OISA) framework that improves the quality and multi-objective performance of radiology report generation models by leveraging self-generated reports, multi-objective preference evaluation, and MODPO multi-objective direct preference optimization loops.
Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models
Mats Faulborn (scieneers GmbH), David Garcia (University of Mannheim)
ClassificationLarge Language ModelPrompt EngineeringText
🎯 What it does: Measuring political bias in large language models, constructing a theory-driven scale, and considering prompt sensitivity.
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Runxuan Liu (Harbin Institute of Technology), Bing Qin (Harbin Institute of Technology)
RetrievalTransformerLarge Language ModelGraphChain-of-Thought
🎯 What it does: Proposes the Ontology-Guided Reverse Thinking (ORT) framework, which leverages LLM to extract intent labels and conditional labels from questions, constructs inverse label reasoning paths, and uses these paths to guide knowledge graph queries, thereby significantly improving the answer coverage and accuracy of KGQA.
Open-World Attribute Mining for E-Commerce Products with Multimodal Self-Correction Instruction Tuning
Jiaqi Li (Southeast University), Sheng Bi (Southeast University)
Recommendation SystemData-Centric LearningLarge Language ModelSupervised Fine-TuningPrompt EngineeringVision Language ModelMultimodalityChain-of-Thought
🎯 What it does: Proposed a multi-modal self-calibrating instruction fine-tuning (MSIT) framework for mining open-ended attributes from product text and images.
Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents
Xiaotian Liu (University of Toronto), Scott Sanner (University of Toronto)
Robotic IntelligenceTransformerLarge Language ModelWorld ModelTextBenchmark
🎯 What it does: Designed an open-world planning method LLM-REGRESS that combines boosted regression with LLM inference, capable of generating executable plans for embodied agents in environments with missing knowledge.
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
Siming Huang (Fudan University), Zili Wang (INF)
Data SynthesisAI Code AssistantTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Propose OpenCoder, providing a complete open-source code LLM training system, including model weights, reproducible pre-training data (RefineCode), synthetic data, instruction tuning data, and intermediate checkpoints, along with the full training process.
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization
Hongliang He (Zhejiang University), Dong Yu (Tencent AI Lab)
OptimizationData-Centric LearningReinforcement Learning from Human FeedbackLarge Language ModelReinforcement LearningAgentic AIVision Language ModelVision-Language-Action ModelImageTextMultimodality
🎯 What it does: Built and trained a multi-modal web agent called OpenWebVoyager, which first mastered the basic navigation capabilities of GPT-4o through imitation learning, and then achieved autonomous improvement via multi-round exploration-feedback-optimization cycles in real web environments.
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport
Minseok Choi (KAIST AI), Jaegul Choo (KAIST AI)
Safty and PrivacyTransformerLarge Language ModelTextBenchmark
🎯 What it does: Studied and implemented an entity-level unlearning method called OPT-OUT, and constructed the first dataset for evaluating this task, ELUDe.
Optimal Transport-Based Token Weighting scheme for Enhanced Preference Optimization
Meng Li (Renmin University of China), Anxiang Zeng (Shopee Pte Ltd)
OptimizationReinforcement Learning from Human FeedbackTransformerLarge Language ModelReinforcement LearningTextBenchmark
🎯 What it does: Propose an unbiased optimal transport-based token weighting scheme (OTPO) that dynamically assigns token weights in direct preference optimization (DPO) to enhance the alignment of large language models (LLMs) with human preferences.
Optimizing Decomposition for Optimal Claim Verification
Yining Lu (University of Notre Dame), Meng Jiang (University of Notre Dame)
OptimizationRecurrent Neural NetworkLarge Language ModelReinforcement LearningText
🎯 What it does: Propose a dynamic decomposition method that learns sub-claim decomposition strategies best matched with verifiers through reinforcement learning within a decomposition-verification framework.
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models
Lior Belenki (Google DeepMind), Kristina Toutanova (Google DeepMind)
OptimizationHyperparameter SearchData-Centric LearningTransformerLarge Language ModelMixture of ExpertsText
🎯 What it does: Improve the optimization of data mixing by constructing a Data Expert Mixture (MDE) to approximate the cross-entropy loss of different pre-trained data mixtures, through training expert models for each data source and weighting their outputs with mixing ratios.
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Linhao Ye (East China Normal University), Liang He (East China Normal University)
RetrievalOptimizationTransformerTextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Propose a three-module retrieval-enhanced multi-hop QA framework Q-DREAM, which collaboratively handles multi-hop questions through three steps: problem splitting, sub-question dependency optimization, and dynamic semantic space retrieval.
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use
Xueyu Hu (Zhejiang University), Fei Wu (Zhejiang University)
TransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningAgentic AIVision Language ModelVision-Language-Action ModelTextMultimodalityReview/Survey PaperBenchmarkChain-of-Thought
🎯 What it does: Reviews operating system agents (OS Agents) based on (multimodal) large language models, systematically organizing aspects such as fundamental concepts, key components, construction methods, evaluation metrics and benchmarks, commercial products, challenges, and future prospects.
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
Qiushi Sun (Shanghai AI Laboratory), Zhiyong Wu (Shanghai AI Laboratory)
GenerationData SynthesisLarge Language ModelSupervised Fine-TuningReinforcement LearningVision Language ModelTextSequentialBenchmark
🎯 What it does: Propose the OS-Genesis data synthesis pipeline, which adopts interaction-driven functional discovery and reverse task synthesis to generate high-quality, diverse GUI trajectory data without requiring manual annotation;
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
Jungwoo Park (Korea University), Jaewoo Kang (Korea University)
OptimizationComputational EfficiencyTransformerLarge Language ModelText
🎯 What it does: Developed an Outlier-Safe Pre-Training (OSP) framework that actively avoids activating outliers during training of large language models, achieving more efficient 4-bit quantization;
OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching
Nghia Huynh Nguyen Hieu (FPT Software AI Center), Van Nguyen (FPT Software AI Center)
Data SynthesisFlow-based ModelAudio
🎯 What it does: Proposed a zero-shot speech synthesis model based on learning prior conditional flow matching, supporting one-step sampling
P^2 Law: Scaling Law for Post-Training After Model Pruning
Xiaodong Chen (Renmin University of China), Jing Zhang (Engineering Research Center of Database and Business Intelligence, MOE, China)
Computational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Investigate and propose the P 2 Law, which describes the relationship between the post-training loss of a pruned model and factors such as model size, amount of post-training data, pruning rate, and pre-pruning loss;
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs
Fakhraddin Alwajih (University of British Columbia), Muhammad Abdul-Mageed (University of British Columbia)
Data SynthesisLarge Language ModelTextBenchmark
🎯 What it does: Constructed a fully human-generated instruction dataset called PALM, covering 22 Arab countries, including Modern Standard Arabic and local dialects, across 20 cultural themes.
Pandora’s Box or Aladdin’s Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models
Jinyang Wu (Tsinghua University), Jianhua Tao (Tsinghua University)
TransformerLarge Language ModelTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Constructed a seven-category system for RAG retrieval noise and proposed the NoiserBench benchmark to evaluate LLM performance under various noise conditions, discovering that certain noises (e.g., illegal sentence noise) can enhance model performance.
Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths
Songlin Zhai (Southeast University), Guilin Qi (Southeast University)
OptimizationExplainability and InterpretabilityRepresentation LearningTransformerLarge Language ModelContrastive LearningText
🎯 What it does: Propose a parameter-aware contrastive learning method based on critical transmission paths for knowledge editing.
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning
Yongxin Xu (Peking University), Yasha Wang (Peking University)
OptimizationTextRetrieval-Augmented Generation
🎯 What it does: Proposes the Parenting framework, which optimizes knowledge selection in retrieval-augmented language models through parameter decoupling and targeted fine-tuning.
PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages
Sina Ahmadi (University of Zurich), Sedighe Zamani Roodsari (Auburn University)
Data-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningContrastive LearningTextBenchmark
🎯 What it does: Constructed the first publicly available parallel corpus for eight extremely low-resource languages in the Middle East (Luri Bakhtiari, Gilaki, Hawrami, Laki, Mazandarani, Southern Kurdish, Talysh, Zazaki), containing approximately 36,384 trilingual sentence pairs.
Partial Colexifications Improve Concept Embeddings
Arne Rubehn (University of Passau), Johann-Mattis List (University of Passau)
Representation LearningGraph Neural NetworkTextGraph
🎯 What it does: Researchers train concept embeddings using a manually curated cross-linguistic co-occurrence network, incorporating partial co-occurrence (prefix/overlap) information into the learning process to improve concept representations.
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Yichen He (ByteDance Seed), Weinan E (Peking University)
RetrievalReinforcement Learning from Human FeedbackTransformerLarge Language ModelAgentic AIText
🎯 What it does: Proposes PaSa, which can mimic researchers' behavior to search for papers, read, traverse citation networks, and automatically obtain comprehensive and accurate academic search results.
Past Meets Present: Creating Historical Analogy with Large Language Models
Nianqi Li, Yanghua Xiao (Fudan University)
GenerationRetrievalTransformerLarge Language ModelTextRetrieval-Augmented Generation
🎯 What it does: Studied how to automatically acquire historical analogies using large language models and proposed a self-reflection framework to reduce hallucinations and stereotypes.
Pattern Recognition or Medical Knowledge? The Problem with Multiple-Choice Questions in Medicine
Maxime Griot (Université catholique de Louvain), Coralie Hemptinne (Université catholique de Louvain)
TransformerLarge Language ModelPrompt EngineeringTextBenchmarkChain-of-Thought
🎯 What it does: Investigated the performance of large language models (LLMs) on medical multiple-choice questions (MCQs), constructing a novel benchmark using the fictional organ Glianorex to isolate the model's memorization and reasoning capabilities.
PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation
Arkadiusz Modzelewski (University of Padua), Giovanni Da San Martino (University of Padua)
Anomaly DetectionTransformerPrompt EngineeringTextChain-of-Thought
🎯 What it does: Proposed Persuasion-Augmented Chain of Thought (PCoT), leveraging persuasion strategy analysis to enhance zero-shot fake news detection capabilities.
People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
Jenna Russell (University of Maryland College Park), Mohit Iyyer (University of Maryland College Park)
ClassificationExplainability and InterpretabilityTransformerLarge Language ModelPrompt EngineeringTextBenchmark
🎯 What it does: This paper investigates the ability of commercial LLMs (GPT-4O, Claude 3.5, O1-PRO) to generate text, employing expert-level annotators to label and explain 300 non-fiction English articles, and comparing them with multiple automatic detectors.
Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models
Ido Cohen (Tel Aviv University), Raja Giryes (Tel Aviv University)
RetrievalExplainability and InterpretabilityVision Language ModelMultimodalityBenchmark
🎯 What it does: Investigate the performance gap of entity knowledge extraction in vision-language models (VLMs) under visual and textual representations, propose the POPVQA dataset, and analyze information flow using mechanism-based explainability methods.
Persistent Homology of Topic Networks for the Prediction of Reader Curiosity
Manuel D. S. Hopp (University of Tübingen), Kou Murayama (University of Tübingen)
TransformerTextTabular
🎯 What it does: This study constructs a text information gap analysis pipeline based on topic networks and persistent homology to quantify curiosity in novels.
Persona Dynamics: Unveiling the Impact of Persona Traits on Agents in Text-Based Games
Seungwon Lim (Yonsei University), Youngjae Yu (Yonsei University)
TransformerLarge Language ModelReinforcement LearningAgentic AIText
🎯 What it does: This paper proposes a PANDA framework that maps Big Five personality traits and Dark Triad personality traits to the decision-making of text game agents through a personality classifier, achieving intelligent agents with personality adaptation.
Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning
Zijian Shao (University of Science and Technology of China), Xiang Wang (University of Science and Technology of China)
Recommendation SystemOptimizationTransformerLarge Language ModelTextBenchmark
🎯 What it does: Construct the RealTravel dataset and propose the Personal Travel Solver (PTS), generating travel plans that satisfy explicit constraints and implicit preferences through a five-module architecture combining LLM and numerical solvers.
Personality-Guided Code Generation Using Large Language Models
Yaoqi Guo (Peking University), Yun Ma (King's College London)
GenerationAI Code AssistantLarge Language ModelPrompt EngineeringTextChain-of-Thought
🎯 What it does: Achieve personality-guided code generation by generating corresponding MBTI personalities for each coding task and instructing the LLM to generate code in the role of that personality.
Personalized Generation In Large Model Era: A Survey
Yiyan Xu (University of Science and Technology of China), Tat-Seng Chua (National University of Singapore)
GenerationLarge Language ModelVision Language ModelDiffusion modelScore-based ModelFlow-based ModelRectified FlowNeural Radiance FieldAuto EncoderGenerative Adversarial NetworkContrastive LearningGaussian SplattingImageVideoTextMultimodalityReview/Survey PaperAudio
🎯 What it does: Systematically reviews personalized generation (PGen) research in the era of large models, proposes a unified framework and multi-level taxonomy, and organizes cross-modal technologies, datasets, and evaluation metrics.
Personalized Text Generation with Contrastive Activation Steering
Jinghao Zhang (Institute of Automation, Chinese Academy of Sciences), Tat-Seng Chua (National University of Singapore)
GenerationComputational EfficiencyTransformerLarge Language ModelContrastive LearningTextRetrieval-Augmented Generation
🎯 What it does: Propose a personalized text generation framework called StyleVector, which requires no training and only stores a single vector. It extracts user writing style vectors using contrastive activation and achieves personalization during inference through linear activation intervention.
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
Yun Luo (Westlake University), Yue Zhang (Westlake University)
RetrievalLarge Language ModelAgentic AITextBenchmarkRetrieval-Augmented Generation
🎯 What it does: This paper proposes the PerSphere framework for multi-perspective retrieval and summarization, helping people break out of information silos.
Phonotomizer: A Compact, Unsupervised, Online Training Approach to Real-Time, Multilingual Phonetic Segmentation
Michael S. Yantosca (University of Houston), Albert M. K. Cheng (University of Houston)
SegmentationAudio
🎯 What it does: Developed an unsupervised online training framework called Phonotomizer based on raw audio for real-time multilingual phoneme segmentation.
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
Xinyu Zhang (Xi'an Jiaotong University), Jun Liu (Xi'an Jiaotong University)
Large Language ModelTextMultimodalityBenchmarkPhysics Related
🎯 What it does: Constructed the PhysReason benchmark, containing 1200 multi-difficulty physics reasoning problems with charts and step-refined problem-solving processes
PIC: Unlocking Long-Form Text Generation Capabilities of Large Language Models via Position ID Compression
Haoran Que (Beihang University), Wenge Rong (Beihang University)
GenerationLarge Language ModelSupervised Fine-TuningTextBenchmark
🎯 What it does: Propose the Position ID Compression (PIC) method by compressing the positional information of input text to enhance the ability of large language models in long text generation (output-long).
PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
Yidan Wang (Chinese Academy of Sciences), Binxing Fang (Guangzhou University)
OptimizationSafty and PrivacyTransformerLarge Language ModelPrompt EngineeringText
🎯 What it does: Proposed a privacy jailbreak attack framework called PIG for large language models, which can effectively extract personal identifying information (PII).
PIGuard: Prompt Injection Guardrail via Mitigating Overdefense for Free
Hao Li (Washington University in St. Louis), Chaowei Xiao (Washington University in St. Louis)
ClassificationAnomaly DetectionComputational EfficiencyData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextBenchmark
🎯 What it does: Propose the NotInject dataset specifically for evaluating over-defensive Prompt Guard models, and design the PIGuard model with the MOF training strategy to address the defect of existing models that easily misclassify normal inputs.
PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning
Zhicong Lu (Chinese Academy of Sciences), Guangluan Xu (China University of Geosciences)
Knowledge DistillationAdversarial AttackTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextBenchmark
🎯 What it does: This paper proposes the PIPER benchmark, which includes 14 event reasoning datasets covering 5 types of event relationships and 4 reasoning formats, with a total of 18,366 samples; constructs 10,000 instruction-tuned data samples for event reasoning; and introduces the D2E-SFT training strategy, combining de-biasing (imagined samples) and self-distillation (context-refined samples) to enhance the performance of LLMs in event reasoning.
Pixel-Level Reasoning Segmentation via Multi-turn Conversations
Dexian Cai (Northeastern University), Soujanya Poria (Singapore University of Technology and Design)
SegmentationExplainability and InterpretabilityLarge Language ModelPrompt EngineeringVision Language ModelImageTextMultimodality
🎯 What it does: Proposed the Pixel-level Reasoning Segmentation task, constructed the corresponding multi-round dialogue dataset PRIST, and designed the MIRAS framework to achieve multi-round interactive pixel-level segmentation and reasoning explanation
PKAG-DDI: Pairwise Knowledge-Augmented Language Model for Drug-Drug Interaction Event Text Generation
Ziyan Wang (Huazhong Agricultural University), Wen Zhang (Huazhong Agricultural University)
GenerationDrug DiscoveryGraph Neural NetworkTransformerLarge Language ModelBiomedical DataRetrieval-Augmented Generation
🎯 What it does: Built a paired knowledge-enhanced language model for generating drug-drug interaction event (DDIE) text.
PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference
Jiaming Ji (Peking University), Yaodong Yang (Hong Kong University of Science and Technology)
Reinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningTextBenchmark
🎯 What it does: This paper proposes the PKU-SAFERLHF dataset and constructs multi-level safety labels and preference data on it to improve the safety alignment of large language models (LLMs); it trains severity-based audit models and a dual (beneficiality and harmlessness separation) RLHF scheme based on this dataset; in comparisons with the BEAVERTAILS dataset and existing audit technologies, it demonstrates higher classification and alignment performance.
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities
Hui Wei (University of California Merced), Fei Liu (Emory University)
Review/Survey Paper
🎯 What it does: This paper summarizes and systematically evaluates current LLM planners, proposes and analyzes six performance metrics: completeness, executability, optimality, representability, generalization, and efficiency, and presents representative methods along with their advantages and disadvantages;
Planning with Diffusion Models for Target-Oriented Dialogue Systems
Hanwen Du (Ohio State University), Xia Ning (Ohio State University)
Large Language ModelDiffusion modelText
🎯 What it does: Proposed a non-sequential planning framework for goal-oriented dialogue systems called DiffTOD, which generates global dialogue trajectories using diffusion models.
Planning-Driven Programming: A Large Language Model Programming Workflow
Chao Lei (University of Melbourne), Krista A. Ehinger (University of Melbourne)
OptimizationAI Code AssistantLarge Language ModelTextBenchmark
🎯 What it does: Propose a two-stage large language model programming workflow named LPW, integrating plan formulation, plan verification, and visible testing to enhance the accuracy of text-to-code generation.
PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning
Zihan Zheng (South China Normal University), Lewei He (South China Normal University)
Large Language ModelTextBenchmark
🎯 What it does: Proposed and implemented the PlanningArena benchmark framework, which simulates real-world application scenarios and integrates multi-dimensional task structures, user profiles, and API/APP toolchains to comprehensively evaluate the planning and tool call capabilities of large language models (LLMs).
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models
Kyeonghyun Kim (Chung-Ang University), YoungBin Kim (Chung-Ang University)
Computational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Propose the PiFi framework, which freezes a single layer of a large language model (LLM), injects it into a small language model (SLM), and then performs task fine-tuning to enhance the performance of SLM.
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean
SungHo Kim (Korea University), SangKeun Lee (Korea University)
Large Language ModelPrompt EngineeringTextBenchmark
🎯 What it does: Proposes KoGEM—a fine-grained evaluation benchmark based on theoretical Korean grammar, comprising 1,524 multiple-choice questions.
PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles
Nikolaos Nikolaidis (Athens University of Economics and Business), Jakub Piskorski (Polish Academy of Science)
ClassificationTransformerSupervised Fine-TuningPrompt EngineeringTextBenchmark
🎯 What it does: This paper constructs the PolyNarrative dataset, providing multi-label narrative annotations at the paragraph level for news articles in multiple languages (Bulgarian, English, Portuguese, Russian) and multiple domains (climate change and the Ukraine-Russia conflict), and conducts benchmark experiments on this dataset.
PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment
Zekun Moore Wang (Beihang University), Wenhao Huang (01.AI)
OptimizationReinforcement Learning from Human FeedbackLarge Language ModelReinforcement LearningPrompt EngineeringContrastive LearningTextBenchmark
🎯 What it does: Propose the PopAlign framework, which constructs six contrastive patterns (prefix contrast, example contrast, guiding contrast, parameter contrast, leaderboard contrast, refinement contrast) across three levels (prompt, model, pipeline) to generate rich preference contrast data without requiring additional human or AI annotations, and use DPO for alignment training.
Position-aware Automatic Circuit Discovery
Tal Haklay (Technion Israel Institute of Technology), Yonatan Belinkov (Technion Israel Institute of Technology)
Explainability and InterpretabilityTransformerLarge Language ModelText
🎯 What it does: Studied a position-aware circuit discovery method called PEAP and proposed a dataset schema for variable-length inputs, utilizing LLM to automatically generate and apply the schema to achieve position-sensitive circuit discovery.
Positional Overload: Positional Debiasing and Context Window Extension for Large Language Models using Set Encoding
Lukas Kinder (Karlsruhe Institute of Technology), Tobias Käfer (Karlsruhe Institute of Technology)
TransformerLarge Language ModelTextBenchmark
🎯 What it does: Propose the Set Encoding method, rewriting the position encoding and attention mask of LLMs to eliminate position bias and expand the context window without additional training.
Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption
Dongjin Park (Chung-Ang University), Joon-Woo Lee (Sejong University)
Safty and PrivacyComputational EfficiencyKnowledge DistillationTransformerText
🎯 What it does: Proposes Powerformer, a homomorphic encryption-based privacy-preserving language model that significantly reduces inference time through multiple technological optimizations while maintaining the same accuracy as the original model.
PPT: A Minor Language News Recommendation Model via Cross-Lingual Preference Pattern Transfer
Yiyang Zhang (University of Science and Technology of China), Nan Chen (University of Science and Technology of China)
Recommendation SystemTransformerLarge Language ModelContrastive LearningText
🎯 What it does: To address the challenge of learning preference patterns in minority language news recommendations due to a lack of user interactions, this paper proposes the PPT model based on cross-lingual preference pattern migration. It leverages the strong encoding capabilities of large language models and achieves accurate recommendations with minimal interactions through news enhancement and cross-lingual alignment.
PQR: Improving Dense Retrieval via Potential Query Modeling
Junfeng Kang (University of Science and Technology of China), Yu Su (Hefei Normal University)
RetrievalTransformerLarge Language ModelText
🎯 What it does: Propose an unsupervised, no-training potential query retrieval framework PQR, which achieves retrieval by modeling the Gaussian mixture distribution of potential queries for documents;
Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria
Yongqi Leng (Tianjin University), Deyi Xiong (Tianjin University)
TransformerLarge Language ModelSupervised Fine-TuningTextBenchmark
🎯 What it does: Construct a large-scale LLM evaluation training dataset with nearly 947K samples, training an evaluator named Praetor that supports Chinese-English bilingualism, two modes (commentary and comparative evaluation), and customizable assessment criteria.
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
Bolei Ma (LMU Munich & Munich Center for Machine Learning), Barbara Plank (LMU Munich & Munich Center for Machine Learning)
TransformerLarge Language ModelTextReview/Survey Paper
🎯 What it does: Reviews and systematizes resources, datasets, task types, and evaluation methods in natural language processing (NLP) for assessing pragmatic competence, focusing on the performance and challenges of large language models (LLMs) in pragmatic phenomena such as implication, reference, speech acts, discourse coherence, and sociopragmatic behaviors.
Pre-Training Curriculum for Multi-Token Prediction in Language Models
Ansar Aynetdinov (Humboldt Universität zu Berlin), Alan Akbik (Humboldt Universität zu Berlin)
TransformerLarge Language ModelText
🎯 What it does: Proposed a pre-training curriculum learning strategy for multi-token prediction (MTP) to help small language models (SLM) better leverage MTP.
Pre-training Distillation for Large Language Models: A Design Space Exploration
Hao Peng (Tsinghua University), Juanzi Li (Tsinghua University)
Knowledge DistillationTransformerLarge Language ModelText
🎯 What it does: In the pre-training phase, knowledge distillation (pre-training distillation, PD) injects logits from a large teacher model into the pre-training of a small model. The system systematically explores four design dimensions: logits processing, loss function, scaling law, and offline/online logits.
Pre^3: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation
Junyi Chen (Shanghai Jiao Tong University), Guihai Chen (Shanghai Jiao Tong University)
GenerationComputational EfficiencyText
🎯 What it does: Developed a structured generation decoding method called Pre3 based on deterministic pushdown automata (DPDA), which directly converts LR(1) grammar into DPDA to achieve efficient parallel transitions;
Predicting Implicit Arguments in Procedural Video Instructions
Anil Batra (University of Edinburgh), Frank Keller (University of Edinburgh)
TransformerLarge Language ModelSupervised Fine-TuningVision Language ModelVideoTextMultimodalityChain-of-Thought
🎯 What it does: Designed and constructed the Implicit-VidSRL dataset for semantic role labeling of multi-step cooking videos, and proposed the iSRL-Qwen2-VL model for implicit argument prediction and next-step action prediction.
Predicting Through Generation: Why Generation Is Better for Prediction
Md Kowsher (University of Central Florida), Niloofar Yousefi (University of Central Florida)
ClassificationGenerationTransformerLarge Language ModelSupervised Fine-TuningText
🎯 What it does: Proposed the PredGen framework, which redefines prediction tasks as token-level generation and integrates techniques such as Scheduled Sampling and Task Adapter;