21 citations · 64 across the 50 of their papers we have counts for
18 papers · 1 filter
Trust-Region Adaptive Policy Optimization
Mingyu Su, Jian Guan, Yuxian Gu +2
Post-training methods, especially Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), play an important role in improving large language models' (LLMs) complex reasoning…
IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +6
Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Nume…
Glyph: Scaling Context Windows via Visual-Text Compression
Jiale Cheng, Yusen Liu, Xinyu Zhang +11
Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context…
Data-Efficient RLVR via Off-Policy Influence Guidance
Erle Zhu, Dazhi Jiang, Yuan Wang +8
Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large language models (LLMs). Current data…
Think Socially via Cognitive Reasoning
Jinfeng Zhou, Zheyu Chen, Shuai Wang +4
LLMs trained for logical reasoning excel at step-by-step deduction to reach verifiable answers. However, this paradigm is ill-suited for navigating social situations, which induce…
Training Language Model to Critique for Better Refinement
Tianshu Yu, Chao Xiang, Mingchuan Yang +8
Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limit…
ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs
Shiyao Cui, Qinglin Zhang, Xuan Ouyang +6
Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but convey…
Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
Zhexin Zhang, Yuhao Sun, Junxiao Yang +4
Fine-tuning on open-source Large Language Models (LLMs) with proprietary data is now a standard practice for downstream developers to obtain task-specific LLMs. Surprisingly, we re…
How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study
Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang +8
Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enhanced reasoning capabilities do n…
BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
Junxiao Yang, Jinzhe Tu, Haoran Liu +9
Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond w…
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues
Jinfeng Zhou, Yuxuan Chen, Jianing Yin +9
Cognitive Restructuring (CR) is a psychotherapeutic process aimed at identifying and restructuring an individual's negative thoughts, arising from mental health challenges, into mo…
StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error
Shu-Xun Yang, Cunxiang Wang, Yidong Wang +3
Evaluating mathematical capabilities is critical for assessing the overall performance of large language models (LLMs). However, existing evaluation methods often focus solely on f…
VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
Jiale Cheng, Ruiliang Lyu, Xiaotao Gu +9
Video generation models have achieved remarkable progress in text-to-video tasks. These models are typically trained on text-video pairs with highly detailed and carefully crafted…
Guiding not Forcing: Enhancing the Transferability of Jailbreaking Attacks on LLMs via Removing Superfluous Constraints
Junxiao Yang, Zhexin Zhang, Shiyao Cui +2
Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs); however, the transferability of these attacks across different models remains limited.…
LongSafety: Evaluating Long-Context Safety of Large Language Models
Yida Lu, Jiale Cheng, Zhexin Zhang +7
As Large Language Models (LLMs) continue to advance in understanding and generating long sequences, new safety concerns have been introduced through the long context. However, the…
AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
Zhexin Zhang, Leqi Lei, Junxiao Yang +13
As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge. While substantial efforts have bee…
HPSS: Heuristic Prompting Strategy Search for LLM Evaluators
Bosi Wen, Pei Ke, Yufei Sun +6
Since the adoption of large language models (LLMs) for text evaluation has become increasingly prevalent in the field of natural language processing (NLP), a series of existing wor…
MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science
Erle Zhu, Yadi Liu, Zhe Zhang +5
Pre-trained on extensive text and image corpora, current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. However, their p…