Papers by Jiaxu Li
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)
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| Challenge: | Existing studies on social biases in language models have focused on only English. |
| Approach: | They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models. |
| Outcome: | The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies. |
ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)
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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu
| Challenge: | Existing studies focus on language-based premises and deduce valid conclusions from visual observations. |
| Approach: | They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning . |
| Outcome: | Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts . |
Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have shown strong capability in understanding and simulating humans’ decisions, suggesting a new way to use LLMs as tools to study social systems. |
| Approach: | They propose a Hybrid GS–LLM matching method that integrates Gale–Shapley with probabilistic acceptance decisions. |
| Outcome: | The proposed method outperforms classical baselines in terms of stability and improves robustness under uncertainty. |
Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity (2024.findings-eacl)
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Eric Khiu, Hasti Toossi, Jinyu Liu, Jiaxu Li, David Anugraha, Juan Flores, Leandro Roman, A. Seza Doğruöz, En-Shiun Lee
| Challenge: | Existing approaches for predicting the performance of NLP models for low-resource languages (LRLs) focus on high-resourced languages, overlooking LRLs and domain shifts. |
| Approach: | They investigate the impact of domain similarity on predicting performance of machine translation models in low-resource languages. |
| Outcome: | The results show that domain similarity has the most important impact on predicting the performance of Machine Translation models. |
Multi-modal Action Chain Abductive Reasoning (2023.acl-long)
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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu
| Challenge: | Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena. |
| Approach: | They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer. |
| Outcome: | The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset. |
Improve LLM-as-a-Judge Ability as a General Ability (2025.emnlp-main)
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| Challenge: | Recent studies focus on generative judges, but only on their judge ability. |
| Approach: | They propose a method that leverages the generative and reasoning capabilities of large language models to evaluate LLM responses across diverse scenarios, providing accurate preference signals. |
| Outcome: | The proposed model performs on RewardBench with only 2% to 40% of the data required by other training frameworks. |
MHALO: Evaluating MLLMs as Fine-grained Hallucination Detectors (2025.findings-acl)
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| Challenge: | Hallucination remains a critical challenge for multimodal large language models, undermining their reliability in real-world applications. |
| Approach: | They propose a benchmark specifically designed for evaluating MLLMs’ capability in performing token-level hallucination detection (FHD) . they use curated training data to train a specialized model that significantly outperforms existing models. |
| Outcome: | The proposed model outperforms existing models in the evaluation of 9 MLLMs and reaches an average F1IoU of 40.59%. |
More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024.findings-acl)
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| Challenge: | Existing studies on bias dataset construction and mitigation focus on one demographic group . in real-world applications, there are more than two demographic groups at risk of the same bias. |
| Approach: | They propose to analyze and reduce biases across multiple demographic groups using a multi-demographic bias dataset. |
| Outcome: | The proposed method can mitigate biases among multiple demographic groups effectively, the authors show . |