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20202026
most citedGender bias and stereotypes in Large Language Models

361 citations · 363 across the 7 of their papers we have counts for

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cs.CL2026

ProText: A benchmark dataset for measuring (mis)gendering in long-form texts

Hadas Kotek, Margit Bowler, Patrick Sonnenberg +1

We introduce ProText, a dataset for measuring gendering and misgendering in stylistically diverse long-form English texts. ProText spans three dimensions: Theme nouns (names, occup…

cs.CL2024

LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

Hadas Orgad, Michael Toker, Zorik Gekhman +4

Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have…

cs.CL2023

DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

David Q. Sun, Artem Abzaliev, Hadas Kotek +3

Controversy is a reflection of our zeitgeist, and an important aspect to any discourse. The rise of large language models (LLMs) as conversational systems has increased public reli…

cs.CL2023361 cited

Gender bias and stereotypes in Large Language Models

Hadas Kotek, Rikker Dockum, David Q. Sun

Large Language Models (LLMs) have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains. This paper investigates LLMs' behavi…

cs.CL2020

Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution

David Q. Sun, Hadas Kotek, Christopher Klein +3

This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing…