2 citations · 6 across the 15 of their papers we have counts for
9 papers · 1 filter
DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models
Jinxiang Xie, Yilin Li, Xunjian Yin +1
Evaluating the performance of Grammatical Error Correction (GEC) models has become increasingly challenging, as large language model (LLM)-based GEC systems often produce correctio…
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models
Jiatao Li, Xinyu Hu, Xunjian Yin +1
The integration of documents generated by LLMs themselves (Self-Docs) alongside retrieved documents has emerged as a promising strategy for retrieval-augmented generation systems.…
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
Xunjian Yin, Xinyi Wang, Liangming Pan +3
The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven agents across various tasks. However, existing agentic systems, wheth…
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
Jiatao Li, Xinyu Hu, Xiaojun Wan
Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integratio…
ContraSolver: Self-Alignment of Language Models by Resolving Internal Preference Contradictions
Xu Zhang, Xunjian Yin, Xiaojun Wan
While substantial advancements have been made in developing large language models (LLMs), achieving control over their behavior can be difficult. Direct preference optimization (DP…
WaterPool: A Watermark Mitigating Trade-offs among Imperceptibility, Efficacy and Robustness
Baizhou Huang, Xiaojun Wan
With the increasing use of large language models (LLMs) in daily life, concerns have emerged regarding their potential misuse and societal impact. Watermarking is proposed to trace…
Enhancing Jailbreak Attacks with Diversity Guidance
Xu Zhang, Dinghao Jing, Xiaojun Wan
As large language models(LLMs) become commonplace in practical applications, the security issues of LLMs have attracted societal concerns. Although extensive efforts have been made…
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation
Xunjian Yin, Xu Zhang, Jie Ruan +1
In recent years, substantial advancements have been made in the development of large language models, achieving remarkable performance across diverse tasks. To evaluate the knowled…
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Haowei Lin, Baizhou Huang, Haotian Ye +7
The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine…