11 citations · 26 across the 9 of their papers we have counts for
9 papers
DIAGPaper: Diagnosing Valid and Specific Weaknesses in Scientific Papers via Multi-Agent Reasoning
Zhuoyang Zou, Abolfazl Ansari, Delvin Ce Zhang +2
Paper weakness identification using single-agent or multi-agent LLMs has attracted increasing attention, yet existing approaches exhibit key limitations. Many multi-agent systems s…
COMPACT: Common-token Optimized Model Pruning Across Channels and Tokens
Eugene Kwek, Wenpeng Yin
Making large language models (LLMs) more efficient in memory, latency, and serving cost is crucial for edge deployment, interactive applications, and sustainable inference at scale…
A Survey on Post-training of Large Language Models
Guiyao Tie, Zeli Zhao, Dingjie Song +23
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational system…
AAAR-1.0: Assessing AI's Potential to Assist Research
Renze Lou, Hanzi Xu, Sijia Wang +15
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, a…
LLMs' Classification Performance is Overclaimed
Hanzi Xu, Renze Lou, Jiangshu Du +6
In many classification tasks designed for AI or human to solve, gold labels are typically included within the label space by default, often posed as "which of the following is corr…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
Large Language Models for Mathematical Reasoning: Progresses and Challenges
Janice Ahn, Rishu Verma, Renze Lou +3
Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the de…
GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models
Jiaxu Zhao, Meng Fang, Shirui Pan +2
Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models (LLMs) in various applications,…
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
Renze Lou, Kai Zhang, Jian Xie +5
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data. This is achieved through two primary schem…