Annual Meeting of the Association for Computational Linguistics · 557 papers
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning
Xue Zhang (Beijing Jiaotong University), Jie Zhou (Tencent Inc)
CodeComputational EfficiencyRepresentation LearningData-Centric LearningReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringTextMultimodalityBenchmarkRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Develop the M-Thinker multilingual reasoning model, trained with reinforcement learning to address the issues of language inconsistency and decreased reasoning accuracy when handling non-English inputs.
ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing
Yichen Cai (Zhejiang University), Lingyun Sun (Zhejiang University)
CodeGenerationExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationRepresentation LearningGraph Neural NetworkTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextGraphRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Designed and implemented ThinkPersona, a role-playing agent (RPA), by constructing a Persona Graph (individual identity knowledge graph) and generating a Question-Reasoning-Answer (QRA) dataset to train LLMs to perform logical and coherent reasoning and answering in dialogues based on individual identity;
Threshold Differential Attention for Sink-Free, Ultra-Sparse, and Non-Dispersive Language Modeling
Xingyue Huang (University of Oxford), Tong Zhao (Snap Inc)
CodeComputational EfficiencyRepresentation LearningTransformerLarge Language ModelPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: A novel attention mechanism called Threshold Differential Attention (TDA) is studied, aiming to solve the attention sink and dispersion issues of Softmax in long contexts, and to achieve extremely sparse and sink-free attention.
Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding
Shunqi Mao (University Of Sydney), Weidong Cai (University Of Sydney)
CodeExplainability and InterpretabilityComputational EfficiencyTransformerPrompt EngineeringVision Language ModelContrastive LearningImageTextMultimodalityBenchmarkRetrieval-Augmented Generation
🎯 What it does: Proposes a decoding method that adaptively magnifies visual regions during inference based on attention, called Perception Magnifier (PM), to reduce visual hallucinations in vision-language models (VLMs);
🎯 What it does: Proposed the concept of Timely Machine, defining test latency as actual wall-clock time, and constructed the TimelyEval benchmark for agentic scenarios. Introduced the Timely-RL reinforcement learning method, enabling LLMs to adaptively adjust their reasoning strategies under a given time budget.
TInR: Exploring Tool-Internalized Reasoning in Large Language Models
Qiancheng Xu (Hong Kong Polytechnic University), Wenjie Li (Southeast University)
CodeAutonomous DrivingOptimizationFederated LearningExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationRepresentation LearningAI Code AssistantReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringMixture of ExpertsTextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Proposes a tool internalization reasoning framework called TInR-U, which internalizes tool knowledge into large language models, achieving the unification of tool identification, invocation, and reasoning through a three-stage training process.
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery
Wenxi Xu (Computer Network Information Center, Chinese Academy of Sciences), Hengshu Zhu (Computer Network Information Center, Chinese Academy of Sciences)
CodeClassificationRepresentation LearningData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringContrastive LearningText
🎯 What it does: Propose the TLSA framework, which aligns text and labels in a shared semantic space using dual encoders, and completes the task of generalized category discovery through label induction, graph structure refinement, and confidence sampling with LLM.
CodeOptimizationExplainability and InterpretabilityComputational EfficiencyMeta LearningTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextChain-of-Thought
🎯 What it does: Studies how to optimize the combination of chain-of-thought (CoT) and in-context learning (ICL) through meta-training in large language models, aiming to enhance the model's reasoning ability on new tasks.
ToMMeR - Efficient Entity Mention Detection from Large Language Models
Victor Morand (Sorbonne Universite), Benjamin Piwowarski (Sorbonne Universite)
CodeRecognitionComputational EfficiencyKnowledge DistillationTransformerLarge Language ModelPrompt EngineeringContrastive LearningTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Designed and trained a lightweight entity mention detector called ToMMeR, which achieves zero-shot entity mention detection by utilizing the hidden states from any early layer of an LLM;
🎯 What it does: Proposes a unified agent framework called ToolOmni, which can achieve efficient tool usage in open-world large tool libraries through active retrieval and execution based on retrieval results;
Hao Xu (Jilin University), Rui Song (Jilin University)
CodeDomain AdaptationRepresentation LearningData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Propose a framework called DOPA that achieves robust context learning in the absence of access to the target domain. It uses an OOD agent constructed from source domain instruction-tuned LLMs and non-tuned LLMs to estimate the similarity between samples and the target domain, and combines diversity constraints for example retrieval;
CodeRetrievalRepresentation LearningTransformerLarge Language ModelAgentic AIPrompt EngineeringContrastive LearningTextMultimodalityAudio
🎯 What it does: Proposed a large-scale fine-grained speech-text alignment dataset FCaps and a pre-trained model CLSP based on multi-granularity contrastive learning.
Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
Xu Chu (Peking University), Weiping Li (Peking University)
CodeOptimizationReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringTextRetrieval-Augmented Generation
🎯 What it does: Significantly improved the model's accuracy and stability when facing different input orders by implementing reinforcement learning based on dual-group advantage on the LLM.
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
Zhihao Gong (Peking University), Dan Hao (Peking University)
CodeComputational EfficiencyAI Code AssistantTransformerLarge Language ModelPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Evaluate the execution efficiency of LLMs in code translation, construct the TRACE benchmark, collect 1,000 efficiency-critical tasks, and systematically evaluate 28 LLMs using stress testing and functional testing.
TRACE: Traversal Retrieval-Augmented Chain of Evidence for Document Understanding
Liqi He, Ping Wang (Wuhan University)
CodeRetrievalExplainability and InterpretabilityComputational EfficiencyRepresentation LearningGraph Neural NetworkTransformerLarge Language ModelPrompt EngineeringVision Language ModelImageTextMultimodalityGraphBenchmarkRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Proposed the TRACE framework (Retrieval + Reasoning Chain via Dual-layer Graphs and Adaptive Path Tracking) and constructed M5BookVQA, a multi-hop, multi-modal, cross-page book question answering benchmark.
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages
Danlu Chen (University Of California San Diego), Freda Shi (University Of Waterloo)
CodeExplainability and InterpretabilityData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningContrastive LearningTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Proposed and evaluated four data difficulty metrics for machine translation in extremely low-resource languages (FRED: Token Fertility, Retrieval Proxy, Pre-training Exposure, Corpus Diversity), and used these metrics to explain the significant differences in translation performance across languages;
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye (Xiamen University), Xiaodong Shi (Xiamen University)
CodeDomain AdaptationComputational EfficiencyKnowledge DistillationReinforcement Learning from Human FeedbackTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextMultimodalityChain-of-Thought
🎯 What it does: Propose a multi-domain machine translation model called TwT, which can dynamically adjust the depth of reasoning based on input difficulty, ranging from intuitive and fast reasoning to deep thinking.
TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs
Ziyi Wang (Adelaide University), Xinyu Wang (Adelaide University)
CodeExplainability and InterpretabilityReinforcement Learning from Human FeedbackTransformerLarge Language ModelAgentic AIPrompt EngineeringTextSequentialFinance RelatedRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Propose the TriEx framework, which records the decision-making process of multi-agent LLMs from three perspectives for interpretability: first-person self-reasoning, second-person opponent belief states, and third-person Oracle auditing, and conducts experimental evaluation in simulated Texas Hold'em Poker.
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
Yilun Liu (University of Queensland), Zi Huang (University of Queensland)
CodeExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationRepresentation LearningTransformerLarge Language ModelReinforcement LearningPrompt EngineeringTextGraph
🎯 What it does: Propose the TRN-R1-Zero framework, which utilizes reinforcement learning to fine-tune large language models individually, enabling zero-shot reasoning on text-rich networks (TRN).
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuqiang Niu (University of Science and Technology of China), Bowen Zhang (Shenzhen Technology University)
CodeClassificationRecommendation SystemAnomaly DetectionGraph Neural NetworkTransformerLarge Language ModelContrastive LearningTextGraphBenchmarkRetrieval-Augmented Generation
🎯 What it does: Constructed a large-scale expert-annotated benchmark for political user stance detection called TwiUSD, and proposed the MRFG framework.
Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding
Diandian Guo (Chinese Academy of Sciences), Yanbing Liu (University of Chinese Academy of Sciences)
CodeClassificationRecognitionExplainability and InterpretabilityComputational EfficiencyRepresentation LearningTransformerLarge Language ModelPrompt EngineeringMixture of ExpertsContrastive LearningTextMultimodalityBenchmarkRetrieval-Augmented Generation
🎯 What it does: This paper proposes the DocMSU-PLUS multimodal sarcasm understanding benchmark and designs the DOSE framework, aiming to unify the perception and reasoning tasks of multimodal sarcasm;
Two-Stage Regularization-Based Structured Pruning for LLMs
Mingkuan Feng (Tsinghua University), Jianhua Tao (Tsinghua University)
CodeComputational EfficiencyKnowledge DistillationTransformerLarge Language ModelText
🎯 What it does: Propose a structured pruning method called TRSP based on two-stage regularization. First, layer importance is learned through learnable layer weights and L1 regularization. Then, knowledge transfer is achieved by regularizing the input-output differences of pruned layers. Finally, low-weight layers are directly pruned without requiring subsequent fine-tuning.
UERLens: Understanding Event Relations in Large Language Models
Yong Guan (North China Electric Power University), Shaoru Guo (Shanxi University)
CodeExplainability and InterpretabilityAdversarial AttackTransformerLarge Language ModelAuto EncoderTextBenchmark
🎯 What it does: Propose the UERLens framework to explain how large models internally encode causal, temporal, and sub-event relationships, construct a contrast dataset called UERBench, use sparse autoencoders to mine hierarchical relationship-sensitive features, and validate their functionality through intervention and training.
UMMF: Protecting Copyright of Large Vision-Language Models through Unlearning-based Multimodal Memorization Fingerprint
Xiaofan Zheng (Peking University), Xiaojun Wan (Xi'an Jiaotong University)
CodeSafty and PrivacySupervised Fine-TuningVision Language ModelContrastive LearningImageTextMultimodality
🎯 What it does: This paper proposes a copyright protection method for LVLMs that embeds implicit fingerprints by learning from the original descriptions of the same image and unlearning (unlearning) from the descriptions of similar neighbor samples during training.
CodeGenerationData SynthesisExplainability and InterpretabilityComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringVision Language ModelDiffusion modelImageTextMultimodalityChain-of-Thought
🎯 What it does: Propose Unified Thinker, a modular framework that decouples reasoning from image generation, utilizing structured reasoning pathways to guide diffusion generators, achieving transferable and pluggable reasoning cores.
UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment
Yuanyuan Wang (Chinese University of Hong Kong), Xixin Wu (Chinese University of Hong Kong)
CodeExplainability and InterpretabilityReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningTextBenchmarkAudio
🎯 What it does: Propose a unified speech reward model, UniSRM, which supports multi-dimensional interpretable evaluation
Unveiling the Unknown: Open-Set Entity Typing via Two-Stage Generation
Hu Chen (Nankai University), Wei Shen (Nankai University)
CodeClassificationGenerationData SynthesisTransformerLarge Language ModelPrompt EngineeringContrastive LearningTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: This paper proposes the Open-Set Entity Type Recognition (OSET) task and designs a two-stage generative model to automatically synthesize high-quality and diverse instances of unknown types for training a unified open-set classifier, thereby enabling simultaneous identification of both known and unknown entity types.
UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages
Pranjal A Chitale, Sunayana Sitaram (Microsoft Corporation)
CodeData SynthesisTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextRetrieval-Augmented Generation
🎯 What it does: This paper constructs UPDESH, a 9.5M instruction-following dataset covering 13 Indian languages (including English), focusing on two subsets: translatable reasoning and culturally localized generation, providing diverse scenarios such as long context, multi-turn dialogue, and cultural reasoning.
VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models
Rui Hu (Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University), Jitao Sang (Unisound AI Technology Co., Ltd.)
CodeRecognitionTransformerLarge Language ModelReinforcement LearningPrompt EngineeringVision Language ModelImageTextMultimodalityBenchmarkRetrieval-Augmented GenerationAudio
🎯 What it does: Propose an end-to-end 'look then listen' speech recognition framework called VAPO, addressing the visual interference problem in multimodal models for speech recognition in presentations.
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
Sangkwon Park (Seoul National University), Sungroh Yoon (Seoul National University)
CodeRetrievalRecommendation SystemExplainability and InterpretabilityComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningAgentic AITextRetrieval-Augmented Generation
🎯 What it does: Proposes Verbal-R3, a two-agent retrieval-augmented generation framework, consisting of a Generator (iterative retrieval and reasoning) and a Verbal Reranker (generates Verbal Annotations for retrieval documents and assigns relevance scores);
Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions
Ruochong Xiong (Peking University), Junfei Liu (Peking University)
CodeAnomaly DetectionExplainability and InterpretabilityComputational EfficiencyTransformerLarge Language ModelSupervised Fine-TuningContrastive LearningTextBenchmark
🎯 What it does: This paper proposes an unsupervised verifiable LLM-generated text detection framework called ProSSD, which extracts low-dimensional semantic features through supervised subspace projection and models the joint distribution of semantics and structure. It utilizes Wasserstein-weighted likelihood ratios to obtain discriminative statistics, significantly improving detection accuracy.
VIGIL: Defending LLM Agents Against Tool-Stream Injection via Verify-Before-Commit
Junda Lin (University of Science and Technology of China), Enhong Chen (University of Science and Technology of China)
CodeSafty and PrivacyReinforcement Learning from Human FeedbackTransformerLarge Language ModelAgentic AITextBenchmarkRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Propose the VIGIL framework, which adopts a verify-before-commit mechanism within LLM agents to defend against tool-flow injection attacks, and constructs the SIREN benchmark to evaluate its performance.
CodeTransformerLarge Language ModelPrompt EngineeringVision Language ModelContrastive LearningImageTextMultimodalityBenchmark
🎯 What it does: This paper introduces VIGNETTE, a bias evaluation benchmark based on visual question answering, containing over 30M synthetic images and covering four paradigms: factual, perceptual, stereotype, and decision-making.
VISTA: Verification In Sequential Turn-based Assessment
Ashley Lewis (Ohio State University), Michael White (Ohio State University)
CodeClassificationAnomaly DetectionExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationRepresentation LearningTransformerLarge Language ModelPrompt EngineeringTextSequentialBenchmarkRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Proposes the VISTA framework, which evaluates the factual accuracy of dialogue systems by extracting, verifying, and classifying assertions sentence by sentence in multi-turn dialogues.
VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation Agents
Xunyi Zhao (Australian Institute for Machine Learning, Adelaide University), Qi Wu (Australian Institute for Machine Learning, Adelaide University)
CodeAutonomous DrivingExplainability and InterpretabilityComputational EfficiencyRepresentation LearningData-Centric LearningReinforcement Learning from Human FeedbackTransformerLarge Language ModelPrompt EngineeringVision Language ModelVision-Language-Action ModelImageTextMultimodalityBenchmarkChain-of-Thought
🎯 What it does: Proposed the VLN-MME framework for unified evaluation of multi-modal large language models (MLLMs) on vision-and-language navigation (VLN) tasks under zero-shot conditions without the need for a simulator.
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models
Rui Wang (MoE Lab, Chinese University of Hong Kong), Kam-Fai Wong (MoE Lab, Chinese University of Hong Kong)
CodeData SynthesisExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationRepresentation LearningReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningAgentic AITextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Propose the WebAggregator framework, which enhances the synthetic reasoning capabilities of deep research agents by automatically generating verifiable deep reasoning QA pairs;
What About the Scene With the Hitler Reference? HAUNT: A Framework to Probe LLMs’ Self-consistency in Closed Domains Via Adversarial Nudge
Arka Dutta (Rochester Institute of Technology), Ashiqur R. KhudaBukhsh (Rochester Institute of Technology)
CodeExplainability and InterpretabilityAdversarial AttackTransformerLarge Language ModelPrompt EngineeringTextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Designed and evaluated a three-step framework, HAUNT, for detecting factual hallucinations in LLMs when they are subjected to dialogic induction in closed domains (movies and books).
What Deserves Memory: Adaptive Memory Distillation for LLM Agents
Wenquan Ma (Fudan University), WenLong Wu
CodeComputational EfficiencyKnowledge DistillationRepresentation LearningTransformerLarge Language ModelPrompt EngineeringTextBenchmarkRetrieval-Augmented Generation
🎯 What it does: Proposes NEMORI, an untrained adaptive memory distillation framework that uses prediction error to determine the future value of LLM agent experiences and uses this as an entry point for memory construction.
🎯 What it does: Propose the SCRL framework, achieving robust test-time reinforcement learning on unlabeled test streams through selective positive and negative pseudo-labels and dynamic reward shaping.
🎯 What it does: Propose an executable knowledge graph (XKG) and integrate it into three agent frameworks to enhance the effectiveness of replicating AI research from papers.
CodeRecommendation SystemTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningContrastive LearningSequential
🎯 What it does: This paper proposes a preference optimization framework called RecPO, which leverages large language models to integrate preference intensity and temporal context information in sequence recommendation tasks, thereby improving the modeling of user preferences and recommendation performance.
CodeSafty and PrivacyExplainability and InterpretabilityAdversarial AttackAI Code AssistantTransformerLarge Language ModelAgentic AIPrompt EngineeringText
🎯 What it does: This paper investigates and verifies that code agents may generate functionally correct but vulnerable patches (FCV patches) in patching without explicit errors, and proposes a single-query black-box injection attack (FCV-Attack) to induce this behavior.
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors
Chenghao Yang (University of Science and Technology of China), Nenghai Yu (University of Science and Technology of China)
CodeExplainability and InterpretabilityKnowledge DistillationRepresentation LearningTransformerLarge Language ModelTextBenchmarkRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Proposes two measurement methods, response pattern similarity (RPS) and action graph similarity (AGS), to quantify the behavioral homogenization caused by knowledge distillation in LLM agents during tool usage.
Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
Dongding Lin (Hong Kong Polytechnic University), Wenjie Li (Hong Kong Polytechnic University)
CodeRecommendation SystemTransformerLarge Language ModelVision Language ModelTextMultimodalityRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: Proposes a framework named SiPeR for scenario-based conversational recommendation, which can infer users' dynamic implicit preferences based on visual scenes and dialogues, and provide appropriate recommendations and responses.
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
Shanghao Li (University Of Illinois Chicago), Philip S. Yu (University Of Illinois Chicago)
CodeExplainability and InterpretabilityComputational EfficiencyRepresentation LearningTransformerLarge Language ModelTextGraphTabular
🎯 What it does: Investigate the internal mechanisms by which LLMs generate hallucinations when reasoning over linearized structured knowledge (such as graphs and tables), propose and quantify two diagnostic metrics—Structural Shortcut Dependency (SSR) and Semantic Alignment Score (SAS), and build a lightweight hallucination detector based on them.
CodeOptimizationExplainability and InterpretabilityRepresentation LearningTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
🎯 What it does: Unify LoRA, partial weight fine-tuning, and activation layer intervention into a dynamic weight update framework, and propose a preference-utility (log-odds) analysis, discovering a consistent trade-off pattern between preference and utility across different control methods, then design the SPLIT objective function to enhance preference while maintaining utility.
Would LLMs be Good Historical Linguists and Chinese Dialect Learners?
Yicheng Liu (Beijing Institute of Technology), Xingchen Zhang (Beijing Institute of Technology)
CodeRepresentation LearningData-Centric LearningReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningReinforcement LearningPrompt EngineeringTextRetrieval-Augmented GenerationChain-of-Thought
🎯 What it does: By introducing historical phonological information of Middle Chinese into large language models, two tasks (conditional phonological change rule induction and sentence-level dialect IPA transcription) are designed, a multi-source dataset is constructed, and LoRA fine-tuning combined with GRPO reinforcement learning is adopted to enhance pronunciation modeling for low-resource Chinese dialects.
WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization
Kunpeng Kang (Qilu University of Technology), Wenpeng Lu (Qilu University of Technology)
CodeRecognitionOptimizationTransformerLarge Language ModelSupervised Fine-TuningTextChain-of-Thought
🎯 What it does: Proposed the WSDPO framework, using chain-of-thought and preference optimization to enhance the performance of generative word sense disambiguation models.
XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts
Jiahao Xu (University of Nevada, Reno), Zikai Zhang (University of Nevada, Reno)
CodeGenerationSafty and PrivacyTransformerLarge Language ModelPrompt EngineeringTextRetrieval-Augmented Generation
🎯 What it does: Propose a multi-bit reversible watermarking method called XMARK for text generated by large language models, which can achieve high message decoding accuracy while maintaining text quality.
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration
Nuo Chen (National University of Singapore), Bingsheng He (National University of Singapore)
CodeAI Code AssistantReinforcement Learning from Human FeedbackTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextReview/Survey PaperBenchmarkRetrieval-Augmented Generation
🎯 What it does: Built a specialized framework and tool called XtraGPT for academic paper paragraph revision, supporting human-machine collaboration, context-awareness, and fine-grained revisions according to writing guidelines.
Your Reasoning Model Knows What Counts: Self-Guided Chain-of-Thought Pruning for Efficient Reasoning
Zi-Ao Ma (Beijing Institute Of Technology), Zhijing Wu (Beijing Institute Of Technology)
CodeExplainability and InterpretabilityComputational EfficiencyKnowledge DistillationReinforcement Learning from Human FeedbackTransformerSupervised Fine-TuningPrompt EngineeringTextBenchmarkChain-of-Thought
🎯 What it does: Propose a self-guided pruning framework named SGP-CoT, which utilizes the model's own likelihood signals to identify and remove redundant reasoning steps, generating more concise chain-of-thought reasoning.
🎯 What it does: Proposes Z3D — a zero-shot 3D visual grounding pipeline capable of locating target objects in 3D scenes using only multi-view images (optional camera poses and depth maps).
ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents
Zechen Li (University of New South Wales), Flora D. Salim (University of New South Wales)
CodeClassificationExplainability and InterpretabilityTransformerLarge Language ModelAgentic AIAuto EncoderTabularTime SeriesRetrieval-Augmented Generation
🎯 What it does: This paper proposes a zero-training motion temporal reasoning framework called ZARA, which interprets multi-sensor temporal data through a statistical knowledge base and retrieval-enhanced LLM agent.
🎯 What it does: Studied zero-shot guqinjianzi recognition, modeling it as a structured visual information extraction task, and proposed the JZ-Glyph framework.
Zero-shot Large Language Models for Automatic Readability Assessment
Riley Grossman (New Jersey Institute of Technology), Yi Chen (New Jersey Institute of Technology)
CodeExplainability and InterpretabilityComputational EfficiencyData-Centric LearningTransformerLarge Language ModelPrompt EngineeringMixture of ExpertsTextTabularSequentialBenchmark
🎯 What it does: Proposed a zero-shot prompting method and the LAURAE integrated model for unsupervised readability assessment, with systematic evaluation on 14 diverse datasets.