Papers by Jiangjie Chen
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
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| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
SELFGOAL: Your Language Agents Already Know How to Achieve High-level Goals (2025.naacl-long)
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Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang
| Challenge: | Existing approaches to improve the performance of language agents without training are not available. |
| Approach: | They propose an automatic approach to break down high-level goals into tree structure of more practical subgoals during interaction with environments while identifying the most useful subgoal. |
| Outcome: | The proposed approach significantly improves the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)
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Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao
| Challenge: | Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios. |
| Approach: | They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality. |
| Outcome: | The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%. |
Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning. |
| Approach: | They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions. |
| Outcome: | The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
Revealing the Barriers of Language Agents in Planning (2025.naacl-long)
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| Challenge: | Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood . |
| Approach: | They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions . |
| Outcome: | The proposed model achieves 15.6% on a real-world planning benchmark. |
Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)
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Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao
| Challenge: | Historical analogies are important abilities that help people make decisions and understand the world. |
| Approach: | They propose a historical analogy acquisition task that uses large language models to acquire historical analogies. |
| Outcome: | The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies. |
EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)
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| Challenge: | Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems. |
| Approach: | They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings. |
| Outcome: | The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks . |
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)
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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao
| Challenge: | Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans. |
| Approach: | They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context. |
| Outcome: | The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning. |
Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)
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| Challenge: | a recent study has focused on simple settings, but their reliability in complex tasks remains understudied. |
| Approach: | They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases . |
| Outcome: | The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base (2024.acl-long)
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| Challenge: | ANALOGYKB is a million-scale analogy knowledge base based on existing knowledge graphs (KGs) based upon relational knowledge triples, we can discover new analogies using the corresponding relations between concepts. |
| Approach: | They propose a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) ANALOGYKB identifies analogies of the same relations and analogies from analogous relations . |
| Outcome: | The proposed model enables both smaller LMs and LLMs to gain better analogical reasoning capabilities. |
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation (2024.acl-long)
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| Challenge: | e.g., GPT-4 still lag behind humans in effective multitasking, a study finds . current textual simulations do not adequately address the notion of time . |
| Approach: | They propose a textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
| Outcome: | The proposed model incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. |
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)
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| Challenge: | Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs. |
| Approach: | They propose a novel approach for joint answer prediction and proof generation using an induced graphical model. |
| Outcome: | The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions. |
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)
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| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
| Approach: | They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints . |
| Outcome: | The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. |
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular. |
| Approach: | They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development. |
| Outcome: | The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks. |
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction (2025.naacl-long)
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| Challenge: | EASYTOOL combines tools from diverse tool documentation into a single tool instruction. |
| Approach: | They propose a framework that transforms tool documentation into a unified tool instruction. |
| Outcome: | EASYTOOL combines extensive tool documentation into a concise tool instruction . it reduces token consumption and improves performance of LLM-based agents . |
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick (2024.acl-long)
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| Challenge: | Large language models generate human-like content, but they also pose a problem with generation diversity, negatively impacting generation diversity and user experience. |
| Approach: | They propose a Logits-Addition watermark and three variants that aim to enhance diversity to overcome generation diversity challenges. |
| Outcome: | The Logits-Addition watermark outperforms the Logits+Trick-based watermark in diversity tests and outperformed other decoding-based methods by 0.1 to 0.3. |
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on word analogies, but they neglect structures that underpin analogical reasoning. |
| Approach: | They propose a task to abduct structures that form an analogy between two systems to evaluate their analogical reasoning abilities. |
| Outcome: | The proposed task is based on 400 scientific analogies from 13 different fields and is compared with a standard SCAR benchmark. |
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (P19-1)
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| Challenge: | Existing generative methods overlook grammatical structure or make factual mistakes in generated texts. |
| Approach: | They propose a template-based method to ensure the readability of generated type descriptions . they also propose measurable metrics to measure the readibility of the generated type description . |
| Outcome: | The proposed method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets. |
Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)
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Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Jankowski, Yanghua Xiao, Deqing Yang
| Challenge: | Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied. |
| Approach: | They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset. |
| Outcome: | The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs. |
Character is Destiny: Can Persona-assigned Language Models Make Personal Choices? (2025.findings-emnlp)
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Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, null Xiaoqingdong, Yanghua Xiao
| Challenge: | Recent research has demonstrated the potential of using LLMs to develop role-playing language agents (RPLAs) however, imitative decision-making necessitates a more nuanced understanding of personas. |
| Approach: | They propose a method that uses persona-based memory retrieval to improve RPLAs. |
| Outcome: | The proposed method significantly advances RPLAs on this task. |