Papers by Yuqi Chen
LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)
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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. |
| Approach: | They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains. |
| Outcome: | The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases (2024.lrec-main)
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| Challenge: | linguists have suggested that some languages are "cooler" than others because of their contexts. |
| Approach: | They propose to omit plurality and definiteness markers in Chinese noun phrases . they build a corpus of Chinese NPs accompanied by its context . |
| Outcome: | The proposed model predicts the plurality and definiteness of Chinese noun phrases (NPs) it shows that speakers drop plurality markers very frequently, and that they are more likely to drop pronouns . |
StructMem: Structured Memory for Long-Horizon Behavior in LLMs (2026.acl-short)
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| Challenge: | Existing memory systems lack structure and efficiency in capturing relationships between events. |
| Approach: | They propose a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. |
| Outcome: | The proposed framework preserves event-level bindings and induces cross-event connections while reducing token usage, API calls, and runtime compared to prior memory systems. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese (2024.emnlp-main)
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| Challenge: | Humans have produced written language for thousands of years, but most computational work is focused on contemporary languages and corpora. |
| Approach: | They propose a pipeline for historical-psychological text analysis in classical Chinese . they propose an indirect contrastive learning approach that fine-tunes pre-trained models . |
| Outcome: | The proposed pipeline outperforms word-embedding-based approaches across all tasks and exceeds prompting with GPT-4 in most tasks. |
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)
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| Challenge: | Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA. |
| Approach: | They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning. |
| Outcome: | The proposed method significantly improves performance in automatic and human evaluation. |
Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning (2025.findings-acl)
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| Challenge: | Existing prompting methods struggle with complex tasks and reasoning stability, limiting their practical deployment. |
| Approach: | They propose a framework that adaptively balances reasoning accuracy and computational efficiency by employing a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary. |
| Outcome: | The proposed framework achieves significant accuracy improvements (8-11%) while maintaining 2-3 times better efficiency than existing verification methods. |
Revisiting Automated Evaluation for Long-form Table Question Answering (2024.emnlp-main)
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| Challenge: | Existing automated metrics for long-form table question answering (LFTQA) are poorly correlated with human judgments and fail to distinguish between factually accurate responses and those that are factual incorrect. |
| Approach: | They propose to use a meta-evaluation dataset to assess the effectiveness of LLM-based LFTQA systems. |
| Outcome: | The proposed meta-evaluation dataset includes 2,988 human-annotated examples. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)
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| Challenge: | Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages. |
| Approach: | They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. |
| Outcome: | The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data. |
Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) generate reasoning paths before answers, but lack a systematic approach to determine optimal number of samples or select the most faithful rationale. |
| Approach: | They propose a framework that evaluates the quality of reasoning and consistency of answers for each generated sample and uses criteria-based stopping and weighted majority voting to guide early stopping decisions and rationale selection. |
| Outcome: | The proposed framework outperforms existing methods while maintaining accuracy. |
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks. |
| Approach: | They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and improves performance . it can extract triplets of aspect terms, sentiments, and opinion terms from review sentences . |
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)
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| Challenge: | Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs. |
| Approach: | They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning. |
| Outcome: | The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |
Prompt-based Zero-shot Text Classification with Conceptual Knowledge (2023.acl-srw)
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| Challenge: | Existing approaches to pre-training language models rely on verbalizers to translate the predicted vocabulary to task-specific labels. |
| Approach: | They propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. |
| Outcome: | The proposed framework outperforms prompt-based approaches on four widely-used datasets for sentiment analysis and topic detection on the same experimental settings. |
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)
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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations. |
| Approach: | They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge. |
| Outcome: | The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld. |