9 papers
Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models
Yilong Xu, Jinhua Gao, Xiaoming Yu +4
Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge. Although crucial, the retriever primarily focuses on semantics…
ALiiCE: Evaluating Positional Fine-grained Citation Generation
Yilong Xu, Jinhua Gao, Xiaoming Yu +3
Large Language Model (LLM) can enhance its credibility and verifiability by generating text with citations. However, existing research on citation generation is predominantly limit…
Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs
Hanxing Ding, Liang Pang, Zihao Wei +2
Hallucinations present a significant challenge for large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited…
Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun +4
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical…
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation
Shicheng Xu, Liang Pang, Huawei Shen +1
Retrieval-augmented generation (RAG) utilizes retrieved texts to enhance large language models (LLMs). Studies show that while RAG provides valuable external information (benefit),…
Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models
Jingcheng Deng, Zihao Wei, Liang Pang +3
Recent knowledge editing methods have primarily focused on modifying structured knowledge in large language models. However, this task setting overlooks the fact that a significant…