30 citations · 177 across the 46 of their papers we have counts for
12 papers · 1 filter
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
Yuankai Li, Jia-Chen Gu, Di Wu +2
Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge. However, as the number of retrieved documents increases, the inp…
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou +4
Existing multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are…
Can Editing LLMs Inject Harm?
Canyu Chen, Baixiang Huang, Zekun Li +12
Large Language Models (LLMs) have emerged as a new information channel. Meanwhile, one critical but under-explored question is: Is it possible to bypass the safety alignment and in…
Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation
Di Wu, Jia-Chen Gu, Fan Yin +2
Retrieval-augmented language models (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. However, there are significant trustworthiness concer…
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu, Jia-Chen Gu, Caitlin Sikora +8
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linea…
Perturbation-Restrained Sequential Model Editing
Jun-Yu Ma, Hong Wang, Hao-Xiang Xu +2
Model editing is an emerging field that focuses on updating the knowledge embedded within large language models (LLMs) without extensive retraining. However, current model editing…
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval
Qian Wang, Jia-Chen Gu, Zhen-Hua Ling
Audio-text retrieval (ATR), which retrieves a relevant caption given an audio clip (A2T) and vice versa (T2A), has recently attracted much research attention. Existing methods typi…
Neighboring Perturbations of Knowledge Editing on Large Language Models
Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang +1
Despite their exceptional capabilities, large language models (LLMs) are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature…
Corrective Retrieval Augmented Generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu +1
Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although…
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
Zhen Li, Xiaohan Xu, Tao Shen +5
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content qual…
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma +4
Model editing is a technique that edits the large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining. While current mod…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…