7 citations · 29 across the 54 of their papers we have counts for
14 papers · 1 filter
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration
Kangxi Wu, Liang Pang, Huawei Shen +1
The black-box nature of large language models (LLMs) poses challenges in interpreting results, impacting issues such as data intellectual property protection and hallucination trac…
Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models
Shicheng Xu, Liang Pang, Yunchang Zhu +2
Vision-language alignment in Large Vision-Language Models (LVLMs) successfully enables LLMs to understand visual input. However, we find that existing vision-language alignment met…
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…
Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent
Junkai Zhou, Liang Pang, Ya Jing +3
Constructing personalized and anthropomorphic agents holds significant importance in the simulation of social networks. However, there are still two key problems in existing works:…
Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation
Jia Gu, Liang Pang, Huawei Shen +1
With the rapid advancement of large language models (LLMs) for handling complex language tasks, an increasing number of studies are employing LLMs as agents to emulate the sequenti…
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
Zihao Wei, Jingcheng Deng, Liang Pang +3
The extensive utilization of large language models (LLMs) underscores the crucial necessity for precise and contemporary knowledge embedded within their intrinsic parameters. Exist…
Qsnail: A Questionnaire Dataset for Sequential Question Generation
Yan Lei, Liang Pang, Yuanzhuo Wang +2
The questionnaire is a professional research methodology used for both qualitative and quantitative analysis of human opinions, preferences, attitudes, and behaviors. However, desi…
Event-aware Video Corpus Moment Retrieval
Danyang Hou, Liang Pang, Huawei Shen +1
Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos using the natural languag…
Stable Knowledge Editing in Large Language Models
Zihao Wei, Liang Pang, Hanxing Ding +3
Efficient knowledge editing of large language models is crucial for replacing obsolete information or incorporating specialized knowledge on a large scale. However, previous method…
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation
Shicheng Xu, Liang Pang, Jun Xu +2
The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generatio…
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
Shicheng Xu, Liang Pang, Mo Yu +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face…
Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement
Danyang Hou, Liang Pang, Huawei Shen +1
Video Corpus Moment Retrieval (VCMR) is a new video retrieval task aimed at retrieving a relevant moment from a large corpus of untrimmed videos using a text query. The relevance b…
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…