Search Results for author: Fuxiao Liu

Found 9 papers, 7 papers with code

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

no code implementations14 Mar 2024 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

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.

Causal Inference Fairness

On the Safety Concerns of Deploying LLMs/VLMs in Robotics: Highlighting the Risks and Vulnerabilities

no code implementations15 Feb 2024 Xiyang Wu, Ruiqi Xian, Tianrui Guan, Jing Liang, Souradip Chakraborty, Fuxiao Liu, Brian Sadler, Dinesh Manocha, Amrit Singh Bedi

However, such integration can introduce significant vulnerabilities, in terms of their susceptibility to adversarial attacks due to the language models, potentially leading to catastrophic consequences.

Language Modelling

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

6 code implementations15 Nov 2023 Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu

Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.

Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps

1 code implementation11 Jul 2023 Fuxiao Liu, Paiheng Xu, Zongxia Li, Yue Feng, Hyemi Song

We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs).

In-Context Learning Sentiment Analysis

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

4 code implementations26 Jun 2023 Fuxiao Liu, Kevin Lin, Linjie Li, JianFeng Wang, Yaser Yacoob, Lijuan Wang

To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts.

Hallucination Visual Question Answering

DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents

1 code implementation9 Jun 2023 Fuxiao Liu, Hao Tan, Chris Tensmeyer

In this work, we propose DocumentCLIP, a salience-aware contrastive learning framework to enforce vision-language pretraining models to comprehend the interaction between images and longer text within documents.

Contrastive Learning document understanding

COVID-VTS: Fact Extraction and Verification on Short Video Platforms

1 code implementation15 Feb 2023 Fuxiao Liu, Yaser Yacoob, Abhinav Shrivastava

We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation.

Fact Checking Fact Selection +1

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