CodeSafty and PrivacyLarge Language ModelSupervised Fine-TuningReinforcement LearningVision Language ModelDiffusion modelImageTextMultimodalityBenchmark
π― What it does: Constructed a multi-modal safety benchmark named VLSBench without visual security information leakage, and revealed the existing VSIL issues in current benchmarks through experiments.
VoxEval: Benchmarking the Knowledge Understanding Capabilities of End-to-End Spoken Language Models
Wenqian Cui (Chinese University of Hong Kong), Irwin King (LIGHTSPEED STUDIOS)
CodeLarge Language ModelTextBenchmarkChain-of-ThoughtAudio
π― What it does: Designed and released the VoxEval benchmark to evaluate the knowledge understanding capabilities of end-to-end speech large models (SLMs), and systematically tested the performance of multiple existing SLMs.
CodeObject DetectionObject TrackingTransformerLarge Language ModelPrompt EngineeringVision Language ModelVideoTextMultimodalityChain-of-Thought
π― What it does: Proposes the definition of the complex video question answering (Complex VQA) task, constructs a dedicated CVQA dataset, and designs the VQAGuider framework to guide multi-modal large language models (MLLMs) to decompose, match VideoAPI, and plan paths by breaking down complex video questions into atomic visual tasks (e.g., video object detection, tracking, action recognition, etc.).
VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism
Congzhi Zhang, Weijiang Yu (Sun Yat Sen University)
CodeReinforcement LearningVision Language ModelMultimodalityBenchmarkChain-of-Thought
π― What it does: Proposed a training-free method called VReST, which enhances the chain-of-thought (CoT) performance of large vision-language models (LVLM) in complex visual reasoning tasks through Monte Carlo Tree Search (MCTS) and a multi-modal self-reward mechanism.
Walk in Othersβ Shoes with a Single Glance: Human-Centric Visual Grounding with Top-View Perspective Transformation
Yuqi Bu (Shenzhen Polytechnic University), Qiong Liu (National University of Singapore)
CodeObject DetectionPose EstimationDepth EstimationRetrievalLarge Language ModelVision Language ModelImageTextBenchmarkChain-of-Thought
π― What it does: Proposes the Human Viewpoint Visual Localization (HVG) task and the corresponding InterRef dataset, and designs a Top-View Enhanced Perspective Transformation (TEP) method that infers human viewpoints from a single robot perspective, addressing the sensitivity of visual reference frameworks to differences between robot and human viewpoints.
Watermarking Large Language Models: An Unbiased and Low-risk Method
Minjia Mao (University of Delaware), Michael Chau (University of Hong Kong)
CodeGenerationSafty and PrivacyTransformerLarge Language ModelText
π― What it does: Proposes a novel unbiased watermarking method called Sampling One Then Accepting (STA-1), and extends it to a stronger version named Sampling M Then Accepting (STA-M); the method randomly divides green and red token lists during LLM generation, samples once, and accepts if it falls into the green list or resamples if it falls into the red list, maintaining the original distribution expectation; during the detection phase, it employs a z-test based on green token counting and provides an upper bound for Type-II error using the Gini index; additionally, it provides a low-risk analysis, proving that STA-1 has lower risk in low-entropy scenarios; experiments verify that STA-1 matches or exceeds existing watermarking methods in terms of text quality, detection efficiency, and anti-attack capabilities.
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
Runqi Qiao (Beijing University Of Posts And Telecommunications), Honggang Zhang (Beijing University Of Posts And Telecommunications)
CodeLarge Language ModelMultimodalityBenchmark
π― What it does: Constructed the WE-MATH benchmark, systematically decomposing visual math problems into single-step subproblems and providing fine-grained evaluation;
Do Xuan Long (National University of Singapore), Min-Yen Kan (National University of Singapore)
CodeTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextReview/Survey PaperChain-of-Thought
π― What it does: Conduct a meta-analysis of natural language prompting-related papers from 2022-2025, constructing an evaluation framework with 21 attributes, analyzing the impact of attributes on LLM performance, and verifying the effectiveness of single-attribute improvements in reasoning tasks through experiments.
What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs
Sangyeop Kim (Coxwave), Kimin Lee (KAIST)
CodeSafty and PrivacyAdversarial AttackTransformerPrompt EngineeringText
π― What it does: This paper investigates the security issues of utilizing long contexts for many-shot jailbreaking in large language models (LLMs), systematically evaluates the impact of different context lengths (up to 128K tokens) on attack effectiveness, and analyzes factors such as example density, theme, harm level, and text form on model vulnerability.
Where Are We? Evaluating LLM Performance on African Languages
Ife Adebara (University of British Columbia), Muhammad Abdul-Mageed (University of British Columbia)
CodeTransformerLarge Language ModelTextBenchmark
π― What it does: Designed and released the SAHARA benchmark to evaluate the performance of African languages in large language models and conducted an empirical analysis of the relationship between language policy and data availability.
π― What it does: Constructed WhiSPA, a model that aligns Whisper's acoustic representations with SBERT text semantics and psychological dimension embeddings through self-supervised contrastive learning, aiming to enable speech models to directly capture deep semantics and psychological information without requiring subsequent text language models.
Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs
Xiang Zhang (University of British Columbia), Dujian Ding (University of British Columbia)
CodeExplainability and InterpretabilityComputational EfficiencyRecurrent Neural NetworkTransformerLarge Language ModelPrompt EngineeringTextChain-of-Thought
π― What it does: This paper clarifies the role of prompts in Chain-of-Thought (CoT) reasoning through theoretical analysis and experiments, and proposes a 'Prompt Space and Answer Space' framework to study how to search for optimal prompts in the prompt space to enhance the logical reasoning performance of large language models (LLMs).
WinSpot: GUI Grounding Benchmark with Multimodal Large Language Models
Zheng Hui (Microsoft), Kazuhito Koishida
CodeRecognitionObject DetectionTransformerVision Language ModelImageTextMultimodalityBenchmark
π― What it does: Developed the first Windows GUI localization benchmark, WinSpot, using a two-phase automatic annotation and manual verification based on a multi-modal large language model (MLLM), generating over 5,000 coordinate-instruction pairs.
π― What it does: Investigated and evaluated three advanced models trained on the PhoAudiobook dataset for zero-shot Vietnamese text-to-speech synthesis.
πΏ-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation
Ankita Gupta (University of Massachusetts Amherst), Brendan OβConnor
CodeClassificationTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringTextBenchmark
π― What it does: This paper constructs a large-scale legal argument stance dataset /u1D6FF-Stance, containing millions of argument context-case summary-stance triplets, and evaluates various NLP methods based on this task.