Papers by Jiaxin Yuan
On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)
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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios. |
| Approach: | They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments. |
| Outcome: | The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings. |
VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models (2026.acl-long)
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| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
| Approach: | They propose a framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations. |
| Outcome: | The proposed framework approximates the empirical Pareto frontier obtained through grid search and generalizes well across pruning methods and VLM architectures. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning (2024.findings-naacl)
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Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass
| Challenge: | Existing methods for surfacing symbolic reasoning capabilities are limited to narrow tasks . arithmetic computations are unnatural to perform in pure language space, and hence present difficulties for LLMs. |
| Approach: | They propose a natural language embedded program framework for solving symbolic reasoning tasks. |
| Outcome: | The proposed framework improves on strong baselines across math and symbolic reasoning, text classification, question answering, and instruction following tasks. |
Beyond Self-Report: Bridging the Intention-Behavior Gap in Critical Thinking Assessment via Interpretable Multi-Agent System (2026.acl-long)
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Zekun Li, Jifan Yu, Haoxuan Li, Ye He, Daniel Zhang-Li, Shangqing Tu, Joy Jia Yin Lim, Yikun Jiang, Jiaxin Yuan, Yu Zhang
| Challenge: | Accurate assessment of critical thinking is limited by the Intention Behavior Gap in psychology . evaluators that measure self-reported competence are limited by multiagent architectures . |
| Approach: | They propose a framework that operationalizes cognitive assessment into an interpretable multi-agent workflow with Assessment Chain-of-Thought. |
| Outcome: | The proposed framework aligns better with human expert ratings than gold-standard inventories on large-scale simulations and human participants. |
Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking. |
| Approach: | They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle. |
| Outcome: | The proposed framework can be extended to a multi-view framework by different signals. |
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings (2026.findings-acl)
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Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti, Gauri Jagatap, Jiaxin Yuan, Sarvesh Baskar, Vijay Kamarshi, Andrea Fanelli, Furong Huang
| Challenge: | Hallucinations in Large Vision-Language Models (LVLMs) are a persistent challenge, stemming from inadequate integration of visual information during multimodal reasoning. |
| Approach: | They propose a visual feature incorporation method that encourages the model to learn visually-informed textual embeddings distinct from those of the base LLM and promotes a more balanced attention distribution. |
| Outcome: | The proposed method significantly reduces hallucinations and fosters more balanced multimodal reasoning. |