71 citations · 291 across the 40 of their papers we have counts for
6 papers · 1 filter
AI Coding Agents Can Reproduce Social Science Findings
Meysam Alizadeh, Mohsen Mosleh, Fabrizio Gilardi +2
Recent anecdotal evidence suggests that AI coding agents can reproduce published findings when provided with original data and code; yet systematic evaluation across social science…
EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences
Kshitish Ghate, Andy Liu, Devansh Jain +5
As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We…
Generative Value Conflicts Reveal LLM Priorities
Andy Liu, Kshitish Ghate, Mona Diab +3
Past work seeks to align large language model (LLM)-based assistants with a target set of values, but such assistants are frequently forced to make tradeoffs between values when de…
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to…
CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models
Giada Pistilli, Alina Leidinger, Yacine Jernite +3
This paper introduces the "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Lang…
Ethical and social risks of harm from Language Models
Laura Weidinger, John Mellor, Maribeth Rauh +20
This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understan…