Papers by Fuxiao Liu
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
| Approach: | This tutorial will review cutting-edge research in MLLMs and examine the impact of ML in learning and reasoning. |
| Outcome: | This course will review cutting-edge research in MLLMs and examine the impact of ML models on learning, learning, and multimodal reasoning. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
Copied to clipboard
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. |
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)
Copied to clipboard
| Challenge: | Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. |
| Approach: | They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models. |
| Outcome: | The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning. |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
Copied to clipboard
Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
Visual News: Benchmark and Challenges in News Image Captioning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Visual News Captioner is an entity-aware model for news image captioning . Unlike standard image captions, news images depict situations where people, locations, and events are of paramount importance. |
| Approach: | They propose a visual news captioner model that integrates visual and textual features to generate captions with richer information such as events and entities. |
| Outcome: | The proposed model can generate captions with richer information such as events and entities. |
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024.naacl-long)
Copied to clipboard
Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
| Challenge: | Existing large language models have limited ability to perform tasks effectively. |
| Approach: | They propose a large-scale multimodal chart instruction dataset with 600k instances supporting diverse tasks and chart types. |
| Outcome: | The proposed LMM achieves state-of-the-art performance on existing chart QA benchmarks. |
COVID-VTS: Fact Extraction and Verification on Short Video Platforms (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for fact checking multimodal information are limited due to the lack of public data. |
| Approach: | They propose a benchmark for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation. |
| Outcome: | The proposed model detects token-level malicious tampering in different modalities and generates explanations. |