10 papers
Summarization is Not Dead Yet
Dongqi Liu, Chenxi Whitehouse, Zheng Zhao +3
The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summar…
Disco-RAG: Discourse-Aware Retrieval-Augmented Generation
Dongqi Liu, Hang Ding, Qiming Feng +6
Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most exis…
SE-Search: Self-Evolving Search Agent via Memory and Dense Reward
Jian Li, Yizhang Jin, Dongqi Liu +9
Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external knowledge. Recent se…
RoleRMBench & RoleRM: Towards Reward Modeling for Profile-Based Role Play in Dialogue Systems
Hang Ding, Qiming Feng, Dongqi Liu +9
Reward modeling has become a cornerstone of aligning large language models (LLMs) with human preferences. Yet, when extended to subjective and open-ended domains such as role play,…
Explanatory Summarization with Discourse-Driven Planning
Dongqi Liu, Xi Yu, Vera Demberg +1
Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do…
What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
Dongqi Liu, Chenxi Whitehouse, Xi Yu +6
Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning. This paper introduces VISTA, a dataset specifically designed…