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Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings — either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.
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Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems. In this perspectives paper, we discuss possible ways for LLMs to support relevance judgments along with concerns and issues that arise. We devise a human–machine collaboration spectrum that allows to categorize different relevance judgment strategies, based on how much humans rely on machines. For the extreme point of ‘fully automated judgments’, we further include a pilot experiment on whether LLM-based relevance judgments corre- late with judgments from trained human assessors. We conclude the paper by providing opposing perspectives for and against the use of LLMs for automatic relevance judgments, and a compromise per- spective, informed by our analyses of the literature, our preliminary experimental evidence, and our experience as IR researchers
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- Award ID(s):
- 1846017
- PAR ID:
- 10473538
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400700736
- Page Range / eLocation ID:
- 39 to 50
- Subject(s) / Keyword(s):
- large language models, relevance judgments, human–machine collaboration, automatic test collections
- Format(s):
- Medium: X
- Location:
- Taipei Taiwan
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3578337.3605136
