Papers by Meiqi Chen
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective (2024.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models have facilitated the development of Multimodal LLMs. |
| Approach: | They propose a causal framework to interpret unimodal biases in visual question answering problems and a framework to integrate information from different modalities and mitigate biase. |
| Outcome: | The proposed framework analyzes visual question answering (VQA) problems to assess their impact on predictions. |
CELLO: Causal Evaluation of Large Vision-Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in large vision-language models have improved causal reasoning abilities . however, current models struggle with tasks like causal reasoning . |
| Approach: | They propose a fine-grained and unified definition of causality involving interactions between humans and objects. |
| Outcome: | The proposed model surpasses traditional commonsense causality by including explicit causal graphs . it also shows that current LVLMs can benefit from a causally inspired prompting strategy . |
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities (2022.acl-demo)
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Yubo Ma, Zehao Wang, Mukai Li, Yixin Cao, Meiqi Chen, Xinze Li, Wenqi Sun, Kunquan Deng, Kun Wang, Aixin Sun, Jing Shao
| Challenge: | Recent Knowledge Graphs (KGs) store billions of world facts in a directed graph, but expression ability of such entity-centric KGs is limited. |
| Approach: | They propose a large-scale multi-modal event knowledge graph named MMEKG that unifies different modalities of knowledge via events. |
| Outcome: | The proposed system unifies different modalities of knowledge via events, which complement and disambiguate each other. |
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification (2022.coling-1)
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| Challenge: | Existing methods to identify event-event causal relations in a document are noisy and require heuristic rules or external tools. |
| Approach: | They propose a document-level event-event causality identification framework that uses heuristic rules to design edges between events. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on two benchmark datasets. |
Figure It Out: Improve the Frontier of Reasoning with Executable Visual States (2026.acl-long)
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| Challenge: | Recent reasoning models fail to capture structural constraints in complex settings. |
| Approach: | They propose a visual-based reasoning system that integrates executable visual construction into multi-turn reasoning via end-to-end reinforcement learning. |
| Outcome: | The proposed model outperforms strong text-only chain-of-thought models on seven mathematical benchmarks and improves by 13.12% on AIME 2025 and 11.00% on BeyondAIME. |
CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)
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| Challenge: | Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures. |
| Approach: | They propose a document-level event causality identification model which annotates central events and incorporates event centrality information into the reasoning network. |
| Outcome: | The proposed model performs high-order reasoning while considering event centrality. |
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation (2024.findings-emnlp)
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| Challenge: | a new evaluation framework for vision-language navigation is proposed . current evaluation standards hinge on endpoint success rates and path alignment metrics . |
| Approach: | They propose a semi-automatic method for CFG construction with Large-Language Models . they induct data spanning five principal instruction categories and analyze them . |
| Outcome: | The proposed framework diagnoses current models for the Vision-Language Navigation task at a finer-grained level. |
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
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| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
| Approach: | They propose a model PAIE for event argument extraction using prompt tuning for extractive objectives. |
| Outcome: | The proposed model can extract arguments with the same role instead of heuristic threshold tuning. |
Improving Large Language Models in Event Relation Logical Prediction (2024.acl-long)
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| Challenge: | Event relation extraction tasks require rigorous logical reasoning and semantic comprehension, a challenge for narrative understanding and reasoning. |
| Approach: | They propose three approaches to endow LLMs with event relation logic to generate more coherent answers across different scenarios. |
| Outcome: | The proposed approach improves on a set of ERE tasks and provides insights for future work. |