149 citations · 475 across the 69 of their papers we have counts for
18 papers · 1 filter
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models
Zhen Zhang, Xinyu Wang, Yong Jiang +7
Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle…
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent
Yangning Li, Yinghui Li, Xinyu Wang +8
Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs). Although prom…
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
Ziyang Chen, Xiaobin Wang, Yong Jiang +4
Question Answering (QA) systems face challenges in handling complex questions that require multi-domain knowledge synthesis. The naive RAG models, although effective in information…
Benchmarking Agentic Workflow Generation
Shuofei Qiao, Runnan Fang, Zhisong Qiu +6
Large Language Models (LLMs), with their exceptional ability to handle a wide range of tasks, have driven significant advancements in tackling reasoning and planning tasks, wherein…
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process
Peng Wang, Xiaobin Wang, Chao Lou +3
In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the r…
Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation
Chaoyi Ai, Yong Jiang, Shen Huang +2
Named entity recognition (NER) models often struggle with noisy inputs, such as those with spelling mistakes or errors generated by Optical Character Recognition processes, and lea…
Knowledge Mechanisms in Large Language Models: A Survey and Perspective
Mengru Wang, Yunzhi Yao, Ziwen Xu +10
Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI. This paper reviews knowledge mechanism analysis from a novel ta…
Retrieved In-Context Principles from Previous Mistakes
Hao Sun, Yong Jiang, Bo Wang +4
In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to…
ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions
Jingheng Ye, Yong Jiang, Xiaobin Wang +5
This paper introduces the task of product demand clarification within an e-commercial scenario, where the user commences the conversation with ambiguous queries and the task-orient…
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario
Feiteng Mu, Yong Jiang, Liwen Zhang +4
Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness, a crucial factor in hum…
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Zile Qiao, Wei Ye, Yong Jiang +5
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest…
RaFe: Ranking Feedback Improves Query Rewriting for RAG
Shengyu Mao, Yong Jiang, Boli Chen +7
As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream…
WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
Peng Wang, Zexi Li, Ningyu Zhang +6
Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing.…
Agent Planning with World Knowledge Model
Shuofei Qiao, Runnan Fang, Ningyu Zhang +7
Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievement…
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
Xiao Li, Yong Jiang, Shen Huang +3
Key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of ar…
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
Zhuo Chen, Xinyu Wang, Yong Jiang +3
In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints inclu…
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
Zhikun Xu, Yinghui Li, Ruixue Ding +7
How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely…
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…