Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 28 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 16 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 103 tok/s Pro
Kimi K2 197 tok/s Pro
GPT OSS 120B 471 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy (2104.07571v2)

Published 15 Apr 2021 in cs.CL

Abstract: The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic). But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.

Citations (10)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.