3 citations · 8 across the 11 of their papers we have counts for
5 papers · 2 filters
LongRAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall
Zehan Qi, Rongwu Xu, Zhijiang Guo +3
Retrieval-augmented generation (RAG) is a promising approach to address the limitations of fixed knowledge in large language models (LLMs). However, current benchmarks for evaluati…
DebateQA: Evaluating Question Answering on Debatable Knowledge
Rongwu Xu, Xuan Qi, Zehan Qi +2
The rise of large language models (LLMs) has enabled us to seek answers to inherently debatable questions on LLM chatbots, necessitating a reliable way to evaluate their ability. H…
Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
Rongwu Xu, Zi'an Zhou, Tianwei Zhang +5
The common toxicity and societal bias in contents generated by large language models (LLMs) necessitate strategies to reduce harm. Present solutions often demand white-box access t…
Course-Correction: Safety Alignment Using Synthetic Preferences
Rongwu Xu, Yishuo Cai, Zhenhong Zhou +6
The risk of harmful content generated by large language models (LLMs) becomes a critical concern. This paper presents a systematic study on assessing and improving LLMs' capability…
Preemptive Answer "Attacks" on Chain-of-Thought Reasoning
Rongwu Xu, Zehan Qi, Wei Xu
Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants furthe…