29 citations · 152 across the 38 of their papers we have counts for
9 papers · 1 filter
The Superalignment of Superhuman Intelligence with Large Language Models
Minlie Huang, Yingkang Wang, Shiyao Cui +2
We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becom…
CharacterBench: Benchmarking Character Customization of Large Language Models
Jinfeng Zhou, Yongkang Huang, Bosi Wen +13
Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs' character c…
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Bosi Wen, Pei Ke, Xiaotao Gu +11
Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to…
From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks
Zhexin Zhang, Junxiao Yang, Yida Lu +5
Large Language Models (LLMs) are known to be vulnerable to jailbreak attacks. An important observation is that, while different types of jailbreak attacks can generate significantl…
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
Jiale Cheng, Yida Lu, Xiaotao Gu +6
Although Large Language Models (LLMs) are becoming increasingly powerful, they still exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding…
Learning Task Decomposition to Assist Humans in Competitive Programming
Jiaxin Wen, Ruiqi Zhong, Pei Ke +3
When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones. To assist humans in repairing…
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen, Zhiliang Tian, Zexin Jian +5
Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. The knowledge boundary (KB) of an LLM limits its factual understanding, beyond wh…
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
Zhexin Zhang, Yida Lu, Jingyuan Ma +8
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs'…
Towards Efficient Exact Optimization of Language Model Alignment
Haozhe Ji, Cheng Lu, Yilin Niu +5
The alignment of language models with human preferences is vital for their application in real-world tasks. The problem is formulated as optimizing the model's policy to maximize t…