Papers by Zhiheng Lyu
Logical Fallacy Detection (2022.findings-emnlp)
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Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schoelkopf
| Challenge: | Existing language models perform poorly on logical fallacy detection . fallacious arguments can lead to disagreements, conflicts, endless debates, and a lack of consensus . |
| Approach: | They propose a task of logical fallacy detection and propose LogicClimate to detect fallacies in text. |
| Outcome: | The proposed task outperforms the best language model on Logic and LogicClimate . human reasoning is marred by logical fallacies, and some exacerbate misinformation . |
FactTrack: Time-Aware World State Tracking in Story Outlines (2025.naacl-long)
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| Challenge: | Existing language models still struggle to reason over long context windows . et al., 2022, show that long context generation is a challenge for LLMs . |
| Approach: | They propose a method for tracking atomic facts and addressing factual contradictions . they use a four-step pipeline to update a world state data structure for each new event . |
| Outcome: | The proposed method outperforms a baseline and fair method on story outlines. |
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)
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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen
| Challenge: | Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset. |
| Approach: | They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. |
| Outcome: | The proposed model outperforms the prior best metrics by 50 points in the test. |
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)
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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen
| Challenge: | Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge. |
| Approach: | They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance. |
| Outcome: | Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points. |
Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis (2024.findings-emnlp)
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Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez Adauto, Rada Mihalcea, Bernhard Schölkopf, Mrinmaya Sachan
| Challenge: | Sentiment analysis aims to identify the sentiment expressed in a piece of text, often in the form of a review. |
| Approach: | They propose a causal discovery task that distinguishes whether a review "primes" the sentiment and a traditional prediction task to model the sentiment using the review as input. |
| Outcome: | The proposed model improves by 32.13 F1 points on a zero-shot five-class SA. |