2 citations · 6 across the 15 of their papers we have counts for
15 papers
Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Yilin Li, Xunjian Yin, Yilin Chen +1
Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this fi…
AGENT-X: Adaptive Guideline-based Expert Network for Threshold-free AI-generated teXt detection
Jiatao Li, Mao Ye, Cheng Peng +2
Existing AI-generated text detection methods heavily depend on large annotated datasets and external threshold tuning, restricting interpretability, adaptability, and zero-shot eff…
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
Jiatao Li, Yanheng Li, Xiaojun Wan
Large Language Models significantly influence social interactions, decision-making, and information dissemination, underscoring the need to understand the implicit socio-cognitive…
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…
History Matters: Temporal Knowledge Editing in Large Language Model
Xunjian Yin, Jin Jiang, Liming Yang +1
The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should…
ALCUNA: Large Language Models Meet New Knowledge
Xunjian Yin, Baizhou Huang, Xiaojun Wan
With the rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now. However, existing benchmarks may not adequately measure th…
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency
Baizhou Huang, Shuai Lu, Weizhu Chen +2
Large language models (LLMs) have exhibited remarkable ability in code generation. However, generating the correct solution in a single attempt still remains a challenge. Prior wor…