59 citations · 142 across the 51 of their papers we have counts for
23 papers · 1 filter
Data Selection for Multi-turn Dialogue Instruction Tuning
Bo Li, Shikun Zhang, Wei Ye
Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsistent, with topic drift, repeti…
SteerRM: Debiasing Reward Models via Sparse Autoencoders
Mengyuan Sun, Zhuohao Yu, Weizheng Gu +2
Reward models (RMs) are critical components of alignment pipelines, yet they exhibit biases toward superficial stylistic cues, preferring better-presented responses over semantical…
Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning
Yi Lu, Deyang Kong, Jianing Wang +8
Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, their test-time compute allocation…
ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
Yutao Mou, Zhangchi Xue, Lijun Li +4
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks. Monitoring step-level tool invocation be…
Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG
Bo Li, Tian Tian, Zhenghua Xu +3
Dynamic retrieval-augmented generation (RAG) allows large language models (LLMs) to fetch external knowledge on demand, offering greater adaptability than static RAG. A central cha…
Decoupling Safety into Orthogonal Subspace: Cost-Efficient and Performance-Preserving Alignment for Large Language Models
Yutao Mou, Xiaoling Zhou, Yuxiao Luo +2
Safety alignment is essential for building trustworthy artificial intelligence, yet it remains challenging to enhance model safety without degrading general performance. Current ap…
AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming
Muxi Diao, Yutao Mou, Keqing He +6
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits…
SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
Zhuohao Yu, Xingru Jiang, Weizheng Gu +4
Watermarking LLM-generated text is critical for content attribution and misinformation prevention. However, existing methods compromise text quality, require white-box model access…
MPL: Multiple Programming Languages with Large Language Models for Information Extraction
Bo Li, Gexiang Fang, Wei Ye +4
Recent research in information extraction (IE) focuses on utilizing code-style inputs to enhance structured output generation. The intuition behind this is that the programming lan…
Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective
Yutao Mou, Xiao Deng, Yuxiao Luo +2
Code security and usability are both essential for various coding assistant applications driven by large language models (LLMs). Current code security benchmarks focus solely on si…
SaRO: Enhancing LLM Safety through Reasoning-based Alignment
Yutao Mou, Yuxiao Luo, Shikun Zhang +1
Current safety alignment techniques for large language models (LLMs) face two key challenges: (1) under-generalization, which leaves models vulnerable to novel jailbreak attacks, a…
SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity
Xiangyu Xi, Deyang Kong, Jian Yang +7
Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and th…
Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation
Zhuohao Yu, Weizheng Gu, Yidong Wang +5
Large Language Models excel at code generation yet struggle with complex programming tasks that demand sophisticated reasoning. To bridge this gap, traditional process supervision…
SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types
Yutao Mou, Shikun Zhang, Wei Ye
Ensuring the safety of large language model (LLM) applications is essential for developing trustworthy artificial intelligence. Current LLM safety benchmarks have two limitations.…
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
Chaoya Jiang, Jia Hongrui, Haiyang Xu +6
This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image…
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction
Dingyao Yu, Yang An, Wei Ye +4
Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality corpora, due to the labor-intensive labeling of spelling errors in real-life human writing or typing scena…
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Zile Qiao, Wei Ye, Yong Jiang +5
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest…
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-e…
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which…
Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks
Bo Li, Wei Ye, Quansen Wang +2
Textual label names (descriptions) are typically semantically rich in many natural language understanding (NLU) tasks. In this paper, we incorporate the prompting methodology, whic…
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
Dingyao Yu, Kaitao Song, Peiling Lu +5
AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classi…
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Yidong Wang, Zhuohao Yu, Zhengran Zeng +10
Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned…
Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Bo Li, Gexiang Fang, Yang Yang +4
The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we foc…