39 citations · 121 across the 78 of their papers we have counts for
20 papers · 1 filter
NILE: Internal Consistency Alignment in Large Language Models
Minda Hu, Qiyuan Zhang, Yufei Wang +7
As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
Boyang Xue, Fei Mi, Qi Zhu +6
Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where…
Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions
Yutao Hou, Yajing Luo, Zhiwen Ruan +4
Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typic…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +5
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Hongru Wang, Rui Wang, Boyang Xue +5
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Prev…
Analysing the Residual Stream of Language Models Under Knowledge Conflicts
Yu Zhao, Xiaotang Du, Giwon Hong +6
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided…
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering
Yu Zhao, Alessio Devoto, Giwon Hong +6
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided…
VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models
Jingtao Cao, Zheng Zhang, Hongru Wang +1
Progress in Text-to-Image (T2I) models has significantly improved the generation of images from textual descriptions. However, existing evaluation metrics do not adequately assess…
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters
Yan Yang, Zeguan Xiao, Xin Lu +5
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release…
OSPC: Detecting Harmful Memes with Large Language Model as a Catalyst
Jingtao Cao, Zheng Zhang, Hongru Wang +3
Memes, which rapidly disseminate personal opinions and positions across the internet, also pose significant challenges in propagating social bias and prejudice. This study presents…
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
Minda Hu, Licheng Zong, Hongru Wang +6
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented…
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
Xiaoming Shi, Zeming Liu, Li Du +6
This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical comm…
AutoPSV: Automated Process-Supervised Verifier
Jianqiao Lu, Zhiyang Dou, Hongru Wang +4
In this work, we propose a novel method named \textbf{Auto}mated \textbf{P}rocess-\textbf{S}upervised \textbf{V}erifier (\textbf{\textsc{AutoPSV}}) to enhance the reasoning capabil…
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
Rui Wang, Fei Mi, Yi Chen +5
The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience c…
Knowledge Conflicts for LLMs: A Survey
Rongwu Xu, Zehan Qi, Zhijiang Guo +4
This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and p…
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval
Hongru Wang, Boyang Xue, Baohang Zhou +5
Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such…
PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification, Retrieval, and Synthesis in Question Answering
Yiming Du, Hongru Wang, Zhengyi Zhao +5
Long-term memory plays a critical role in personal interaction, considering long-term memory can better leverage world knowledge, historical information, and preferences in dialogu…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +6
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions
Hongru Wang, Boyang Xue, Baohang Zhou +5
Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such a…
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
Hongru Wang, Wenyu Huang, Yang Deng +6
Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-…