Papers by Yinya Huang
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)
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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu
| Challenge: | Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning. |
| Approach: | They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
| Outcome: | The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
DAGN: Discourse-Aware Graph Network for Logical Reasoning (2021.naacl-main)
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| Challenge: | Recent QA with logical reasoning questions requires passage-level relations among the sentences. |
| Approach: | They propose a discourse-aware graph network that aggregates passage-level clues for QA by using discourse-based information. |
| Outcome: | The proposed model achieves competitive results on two logical reasoning QA datasets. |
Test of Time: Rethinking Temporal Signal of Benchmark Contamination (2026.acl-long)
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Terry Jingchen Zhang, Gopal Dev, Ning Wang, Max Obreiter, Punya Syon Pandey, Keenan Samway, Wenyuan Jiang, Yinya Huang, Bernhard Schölkopf, Mrinmaya Sachan, Zhijing Jin
| Challenge: | Existing work on benchmarks containing publicly available information has been interpreted as a temporal signal for benchmark contamination. |
| Approach: | They show that LLM-transformed questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same documents. |
| Outcome: | The proposed model can produce different temporal patterns compared to fill-in-the-blank questions retrieved from the same documents. |
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)
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| Challenge: | Existing LLMs are delicate and elusive in prompt words and styles. |
| Approach: | They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning . |
| Outcome: | The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality. |
ATG: Benchmarking Automated Theorem Generation for Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models (LMs) can generate new or reusable theorems, but their ability to generate new theorels is under-explored. |
| Approach: | They propose to use Metamath library to generate new theorems that can be saved as reusable knowledge for future theoretical proving. |
| Outcome: | The proposed benchmark evaluates whether an agent can generate valuable (and possibly brand new) theorems that are applicable for downstream theoretic proving as reusable knowledge. |
Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification (2026.eacl-long)
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| Challenge: | Large language models (LLMs) are increasingly being applied to causal reasoning tasks. |
| Approach: | They propose a symbolic verification framework that checks whether LLM-generated causal expressions are derivable from a given causal graph using do-calculus and probability theory. |
| Outcome: | The proposed framework can recover correct answers that would otherwise be marked incorrect due to superficial differences. |
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promising results in various domains, but their practical application in industry-relevant operations research presents significant challenges and opportunities. |
| Approach: | They propose a cognitive-inspired framework that enhances optimization through counterfactual reasoning . they use a workflow that transforms requirements into mathematical models and executable solver code . |
| Outcome: | Experiments show that ORMind outperforms existing methods in the NL4Opt dataset and ComplexOR dataset. |
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure (2022.emnlp-main)
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| Challenge: | Current explanation datasets often employ synthetic data with simple reasoning structures. |
| Approach: | They propose a comprehensive logical reasoning explanation form that incorporates three main components to better fit the human cognitive process. |
| Outcome: | The proposed model performs better than existing models on real-life scenarios, but is more challenging for the current models. |