Papers by Yuchen Jiang
TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)
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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation. |
| Approach: | They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting. |
| Outcome: | The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods. |
Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
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| Challenge: | Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited. |
| Approach: | They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages. |
| Outcome: | The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages. |
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs (2025.acl-long)
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| Challenge: | Recent studies have focused on prompt engineering to extract sentence embeddings from large language models (LLMs) but these models are mostly decoder-only and the earlier tokens in the sentence cannot attend to the latter, resulting in biased encoding of sentence information and cascading effects on the final decoded token. |
| Approach: | They propose a plug-and-play and training-free technique that prepends each layer’s decoded sentence embedding to the beginning of the sentence in the next layer’ s input. |
| Outcome: | The proposed technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs while incurring negligible additional inference cost. |
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data (2020.emnlp-main)
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| Challenge: | Pre-trained language models suffer from severe miscalibration for both in-distribution and out-of-difference data due to over-parameterization. |
| Approach: | They propose a regularized method to improve in-distribution and out-of-distance calibrations by using on-manifold regularization and off-manfold regularisation. |
| Outcome: | The proposed method outperforms existing methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models (2026.findings-acl)
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Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, Qing Li
| Challenge: | Existing benchmarks for Large Multimodal Models (LMMs) are constrained by static representations, inadequately evaluating their ability to understand time-sensitive knowledge. |
| Approach: | They propose a benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types to evaluate temporal awareness along 6 key dimensions and 11 challenging tasks. |
| Outcome: | The proposed benchmark measures temporal awareness along 6 key dimensions and 11 tasks, while most open-source LMMs still lack time understanding ability. |
AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning (2024.acl-long)
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Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Jiang, Chengfei Lv, Huajun Chen
| Challenge: | Existing language agent systems struggle with costly data reliance and need multiple models for multiple functions. |
| Approach: | They propose an automatic agent learning framework for QA that synthesizes planning trajectories without human intervention. |
| Outcome: | The proposed framework outperforms existing models on question-answering tasks with a division-of-labor strategy. |
LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning (2025.acl-long)
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Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao
| Challenge: | LogicPro is a data synthesis method that uses LeetCode-style algorithm problems and their corresponding Program solutions to generate complex logic data. |
| Approach: | They propose a new method which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize complex logic data in text format. |
| Outcome: | The proposed method outperforms existing models for BBH27, LogicBench, DROP, AR-LSAT, and GSM8K, and a wide range of reasoning datasets. |
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models (2026.findings-eacl)
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| Challenge: | Text-to-image models generate harmful content when unsafe prompts are submitted . authors propose a method to jailbreak text-to image models with safety guardrails . |
| Approach: | They propose a method to jailbreak text-to-image models with safety guardrails . they use a fine-tuned large language model to generate adversarial prompts based on unsafe prompts. |
| Outcome: | The proposed method bypasses safety guardrails and outperforms existing no-box attacks . the proposed method generates adversarial prompts efficiently after fine-tuning the model . |
Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models (2026.acl-long)
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Eric Hanchen Jiang, Levina Li, Frank Wan, Xiao Liang, Sophia Yin, Yuchen Wu, Xinfeng Li, Yizhou Sun, Wei Wang, Kai-Wei Chang, Ying Nian Wu
| Challenge: | Existing frameworks rely on static or rule-based topologies that fail to adapt to task requirements. |
| Approach: | They propose a generative framework that generates highly task-adaptive topologies . they validated the framework on multiple benchmarks and validated it on multiple platforms . |
| Outcome: | The proposed framework outperforms existing frameworks in task-adaptive communication topologies. |
ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024.lrec-main)
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| Challenge: | Input too many potential entity types would distract the model inevitably. |
| Approach: | They propose to use a generative model to exploit entity types' merit on promoting NER task by appending a type matching model to identify the entity types most likely to appear in the sentence. |
| Outcome: | The proposed framework exploits entity types' merit on promoting NER task by adding auxiliary task to the model to discover the entity types. |
DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (2025.findings-emnlp)
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Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang
| Challenge: | Existing approaches to align large language models with human preferences are noisy and varying in importance of preference samples. |
| Approach: | a new method enhances reward modeling by learning to dynamically weigh preference data. |
| Outcome: | a new method improves the performance of large language models with human preferences . it initializes data importance and iteratively refines them to maximize validation performance. |
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering (2025.acl-long)
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| Challenge: | Existing studies focus on prompt engineering to encode the full semantics of a sentence into the embedding of the last token. |
| Approach: | They propose a technique that introduces an extra auxiliary prompt to elicit better sentence embedding . they propose to use the hidden state of the token as the sentence embedded in LLMs . |
| Outcome: | The proposed technique can improve performance of existing prompt-based methods on STS tasks and downstream classification tasks. |
Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)
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| Challenge: | Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation. |
| Approach: | They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions. |
| Outcome: | The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code. |
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)
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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou
| Challenge: | Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features. |
| Approach: | They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. |
| Outcome: | The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances. |
A Structured Span Selector (2022.naacl-main)
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| Challenge: | a typical approach to natural language processing tasks involves selecting text spans and making decisions about them. |
| Approach: | They propose a grammar-based structured span selection model which learns to make use of partial span annotations. |
| Outcome: | The proposed model improves on two popular span prediction tasks. |
Do LLMs Know and Understand Domain Conceptual Knowledge? (2025.findings-emnlp)
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| Challenge: | Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships. |
| Approach: | They introduce a Neighbor Semantic Structure (NSS) and a Chain-of-Thought prompting method to evaluate the effectiveness of various Large Language Models (LLMs) in generating concept sememe trees. |
| Outcome: | The proposed method guides LLMs through an analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees. |
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References (2024.naacl-long)
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Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, Furu Wei
| Challenge: | Existing evaluation benchmarks with limited references may not accurately reflect the quality of the model’s hypotheses. |
| Approach: | They propose a method to enrich evaluation benchmarks by diversifying the expression of a single reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |
| Outcome: | The proposed method can enhance evaluation benchmarks by diversifying the expression of reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |