Papers by Xunjian Yin
Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement (2025.acl-long)
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| Challenge: | Existing agentic systems cannot search the whole design space due to the restriction of human-designed components. |
| Approach: | They propose a Gödel Agent framework that allows agents to recursively improve themselves without relying on fixed algorithms or fixed algorithms. |
| Outcome: | The proposed framework surpasses manual crafted agents in performance, efficiency, and generalizability. |
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods for evaluation of natural language generation tasks lack reliable data. |
| Approach: | They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation. |
| Outcome: | The proposed corpus can perform flexible and interpretable evaluations without references and surpasses existing models. |
Exploring Context-Aware Evaluation Metrics for Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing studies on machine translation evaluation focused on quality of individual sentences, while neglecting the importance of contextual information. |
| Approach: | They propose a context-aware machine translation evaluation metric called Cont-COMET . they use the COMET framework to consider the preceding and subsequent contexts of the sentence . |
| Outcome: | The proposed metric improves system-level and segment-level evaluations on the official WMT framework. |
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency (2025.findings-acl)
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| Challenge: | Existing benchmarks for knowledge editing in multimodal large language models focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge. |
| Approach: | They propose a decomposed definition of multimodal knowledge and a benchmark to evaluate it. |
| Outcome: | The proposed method reveals that it is difficult to define multimodal knowledge editing in LLMs. |
ALCUNA: Large Language Models Meet New Knowledge (2023.emnlp-main)
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| Challenge: | Existing benchmarks do not adequately measure large-scale language models’ capabilities when faced with new knowledge. |
| Approach: | They propose a benchmark called ALCUNA to evaluate LLMs' ability to handle new knowledge by altering existing entity attributes and relationships. |
| Outcome: | The proposed approach generates new knowledge by altering existing entity attributes and relationships, resulting in artificial entities distinct from real-world entities. |
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models (2025.findings-naacl)
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| Challenge: | Recent research focuses on optimizing the use of Self-Docs with their inherent properties remaining underexplored. |
| Approach: | They develop a taxonomy to compare the effectiveness of different types of Self-Docs and explore strategies for combining them with external sources. |
| Outcome: | The proposed model can supplement retrieved content and provide a powerful way to improve knowledge-intensive question answering tasks. |
Error-Robust Retrieval for Chinese Spelling Check (2024.lrec-main)
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| Challenge: | Chinese Spelling Check (CSC) aims to detect and correct spelling errors in Chinese texts . current methods may not fully leverage existing datasets, resulting in insufficient annotated data . |
| Approach: | They propose a plug-and-play retrieval method with error-robust information for Chinese Spelling Check . they employ multimodal representations that fuse phonetic, morphologic, and contextual information . |
| Outcome: | The proposed method improves on the SIGHAN benchmarks on Chinese spelling check (CSC) the proposed method is based on training data and lacks adequate parallel corpora . |
Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)
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| Challenge: | Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC . |
| Approach: | They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC . |
| Outcome: | The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results . |
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation (2024.acl-long)
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| Challenge: | Recent advances in large language models have improved performance across tasks . however, the sensitivity of LLMs to prompt leads to unreliability of evaluation results . |
| Approach: | They propose a new concept to evaluate language models with a fixed question or limited paraphrases as the query. |
| Outcome: | The proposed method outperforms existing benchmarks on multiple language models . it avoids prompt sensitivity, rendering models more reliable and robust . |
DAMON: A Dialogue-Aware MCTS Framework for Jailbreaking Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for multi-turn attacks mainly utilize a predefined dialogue pattern, limiting their effectiveness in realistic situations. |
| Approach: | They propose a multi-turn jailbreak attack method that leverages Monte Carlo Tree Search to explore multi-turned conversational spaces and identifies sub-instruction sequences that induce harmful responses. |
| Outcome: | The proposed method can induce undesired behaviors across five LLMs and three datasets. |
LEDOM: Reverse Language Model (2026.acl-long)
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Xunjian Yin, Sitao Cheng, Yuxi Xie, Xinyu Hu, Li Lin, Xinyi Wang, Liangming Pan, William Yang Wang, Xiaojun Wan
| Challenge: | Autoregressive language models are trained exclusively left-to-right, yet they are limited in their ability to factorize text. |
| Approach: | They propose a purely reverse autoregressive language model that factorizes text as a product of left-to-right conditionals. |
| Outcome: | The proposed model can be used to score forward outputs using reverse posterior estimates. |
How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? (2022.acl-long)
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| Challenge: | Existing models for data-to-text generation are based on pipelines and end-to end architectures. |
| Approach: | They use multidimensional quality metrics to evaluate models on end-to-end data-totext generation and compare their performance against pipeline models. |
| Outcome: | The proposed model improves in Omission and Inaccuracy Extrinsic errors but increases errors such as Addition. |