38 citations · 46 across the 18 of their papers we have counts for
20 papers
Constructing Challenging Browser-Use Tasks by Controlled Environment Interventions
Xunjian Yin, Tianchen Guan, Jinao Wang +8
As browser-use agents improve, benchmarks keep pace by adding new tasks, websites, and applications. Doing so is costly, and because many factors change at once, it is unclear what…
Lazy Grounding: Attacking Search Agents with Factual Evidence
Yulin Zhang, Yukun Huang, Sanxing Chen +4
Search agents mitigate hallucination by grounding their answers in retrieved web results. However, retrieval-based approaches also introduce an attack surface: agents may cite misi…
Coding Agents are Effective Long-Context Processors
Weili Cao, Xunjian Yin, Bhuwan Dhingra +1
Large Language Models (LLMs) have demonstrated remarkable progress in scaling to access massive contexts. However, the access is via the latent and uninterpretable attention mechan…
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…
LEDOM: Reverse Language Model
Xunjian Yin, Sitao Cheng, Yuxi Xie +6
Autoregressive language models are trained exclusively left-to-right. We explore the complementary factorization, training right-to-left at scale, and ask what reasoning patterns e…
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…
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…
COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement
Yuxi Xie, Anirudh Goyal, Xiaobao Wu +5
Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. However, existing approaches typically im…
Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models
Sitao Cheng, Liangming Pan, Xunjian Yin +2
Large language models (LLMs) encode vast amounts of knowledge during pre-training (parametric knowledge, or PK) and can further be enhanced by incorporating contextual knowledge (C…
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…
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…
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability
Xinyu Hu, Li Lin, Mingqi Gao +2
The evaluation of natural language generation (NLG) tasks is a significant and longstanding research area. With the recent emergence of powerful large language models (LLMs), some…
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Junzhe Zhang, Huixuan Zhang, Xunjian Yin +4
Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, which can manifest as misreading and misrecognition errors due to the complexity of…
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…
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…
A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check
Xunjian Yin, Xiaojun Wan
With the development of pre-trained models and the incorporation of phonetic and graphic information, neural models have achieved high scores in Chinese Spelling Check (CSC). Howev…
Human-like Summarization Evaluation with ChatGPT
Mingqi Gao, Jie Ruan, Renliang Sun +3
Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory. In this study, we explored ChatGPT's ability to perform human-lik…
Error-Robust Retrieval for Chinese Spelling Check
Xunjian Yin, Xinyu Hu, Jin Jiang +1
Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications. However, it is confronted with the challenges of i…