3 citations · 5 across the 14 of their papers we have counts for
7 papers · 1 filter
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning
Renxi Wang, Honglin Mu, Liqun Ma +5
Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchma…
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
Haonan Li, Xudong Han, Zenan Zhai +32
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic le…
Explore the Reasoning Capability of LLMs in the Chess Testbed
Shu Wang, Lei Ji, Renxi Wang +4
Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabiliti…
ToolGen: Unified Tool Retrieval and Calling via Generation
Renxi Wang, Xudong Han, Lei Ji +3
As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation. Traditional method…
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Lizhi Lin, Honglin Mu, Zenan Zhai +9
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…
Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
Renxi Wang, Haonan Li, Xudong Han +2
Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for langu…
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Renxi Wang, Haonan Li, Minghao Wu +4
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datas…