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20212026
most citedBias and Fairness in Large Language Models: A Survey

59 citations · 240 across the 38 of their papers we have counts for

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Showing 2023 · cs.CLShow all

8 papers · 2 filters

cs.CL2023★ 1 cited

GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions

Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi +3

There is growing interest in systems that generate captions for scientific figures. However, assessing these systems output poses a significant challenge. Human evaluation requires…

cs.CL2023★ 3 cited

ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

Yuchen Zhuang, Xiang Chen, Tong Yu +5

Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can intera…

cs.CL2023★ 59 cited

Bias and Fairness in Large Language Models: A Survey

Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6

Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…

cs.CL2023★ 3 cited

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo +4

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressiv…

cs.CL2023★ 14 cited

Knowledge Graph Prompting for Multi-Document Question Answering

Yu Wang, Nedim Lipka, Ryan A. Rossi +3

The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this…

cs.CL2023★ 1 cited

FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback

Ashish Singh, Ashutosh Singh, Prateek Agarwal +10

Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from docum…

cs.CL2023

Learning the Visualness of Text Using Large Vision-Language Models

Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer +2

Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and ge…

cs.CL2023

Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization

Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi +6

Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation…