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cs.CL2024

Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI

Hangzhi Guo, Pranav Narayanan Venkit, Eunchae Jang +7

The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inher…

cs.CL2024

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

Jiangshu Du, Yibo Wang, Wenting Zhao +37

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…

cs.CL2024

An Audit on the Perspectives and Challenges of Hallucinations in NLP

Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta +5

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Throu…

cs.CL20231 cited

The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam +4

We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our i…

cs.CL20233 cited

Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models

Pranav Narayanan Venkit, Mukund Srinath, Shomir Wilson

We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framewor…