Papers by Zhaoyi Li
What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in reasoning with large language models have popularized Long Chain-of-Thought (LCoT) a framework that converts sequential LCoTs into hierarchical tree structures enables deeper structural analysis of LLM reasoning. |
| Approach: | They propose a framework that converts sequential LCoTs into hierarchical tree structures and enables deeper structural analysis of LLM reasoning. |
| Outcome: | The proposed framework can be used to analyze LLM reasoning in a variety of tasks and models. |
Human-in-the-loop Schema Induction (2023.acl-demo)
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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch
| Challenge: | Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones. |
| Approach: | They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches . |
| Outcome: | The proposed system transfers to new domains more easily than previous approaches and reduces human curation. |
Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing (2024.emnlp-main)
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| Challenge: | Existing studies have focused on using Large Language Models to improve translation quality . language mismatch and repetition are two of the main problems with LLMs . |
| Approach: | They propose to leverage model editing methods to reduce language mismatch and repetition . they propose to fetch intersections of locating results under different language settings . |
| Outcome: | The proposed methods reduce language mismatch and repetition ratios and enhance translation quality in most cases. |
LLMSurgeon: Diagnosing Data Mixture of Large Language Models (2026.acl-long)
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Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen
| Challenge: | a lack of transparency in large language models makes auditing their "digital DNA" difficult. |
| Approach: | They propose a framework that casts DMS as an inverse problem under label-shift assumption . they propose LLMScan, a recipe-verifiable evaluation suite built from open-source LLMs . |
| Outcome: | The proposed framework casts DMS as an inverse problem under label-shift assumption . compared with existing frameworks, it recovers domain mixtures with high fidelity . |
Learning to Substitute Spans towards Improving Compositional Generalization (2023.acl-long)
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| Challenge: | despite the rising prevalence of neural sequence models, there is a deficiency in compositional generalization. |
| Approach: | They propose a compositional augmentation strategy that enables multi-grained composition of substructures in the whole training set. |
| Outcome: | The proposed strategy outperforms existing strategies on three compositional generalization benchmarks. |
Understanding and Patching Compositional Reasoning in LLMs (2024.findings-acl)
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| Challenge: | LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. |
| Approach: | They propose a lightweight method to patch compositional reasoning errors via editing the located MHSA modules in LLMs. |
| Outcome: | The proposed method can be used to patch compositional reasoning errors using MHSA modules located within the layers of the LLMs. |
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation (2024.acl-long)
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| Challenge: | Existing MCTG methods face a noticeable performance drop in compositional testing. |
| Approach: | They propose a benchmark to evaluate compositional generalization of MCTG methods by combining multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol. |
| Outcome: | The proposed framework improves compositional generalization performance by 3.64% and 94.4% in compositional testing. |
Choice-75: A Dataset on Decision Branching in Script Learning (2024.lrec-main)
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| Challenge: | Existing studies only consider scripts as linear developments of events, ignoring the potential branches that arise due to people’s circumstantial choices. |
| Approach: | They propose a benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. |
| Outcome: | The proposed benchmarks show that they perform well in hard scenarios, but there is still significant headroom in hard ones. |
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users. |
| Approach: | They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages. |
| Outcome: | The proposed model improves sentiment forecasting at microscopic and macroscopic levels. |