4 citations · 10 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation
Zijun Wang, Haoqin Tu, Jieru Mei +3
This paper studies the vulnerabilities of transformer-based Large Language Models (LLMs) to jailbreaking attacks, focusing specifically on the optimization-based Greedy Coordinate…
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Bo Peng, Daniel Goldstein, Quentin Anthony +27
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-value…
AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability
Siwei Yang, Bingchen Zhao, Cihang Xie
This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first sear…
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
Bingchen Zhao, Haoqin Tu, Chen Wei +2
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a do…
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics
Haoqin Tu, Bingchen Zhao, Chen Wei +1
Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual respon…