Social Bias Evaluation for Large Language Models Requires Prompt Variations
- URL: http://arxiv.org/abs/2407.03129v1
- Date: Wed, 3 Jul 2024 14:12:04 GMT
- Title: Social Bias Evaluation for Large Language Models Requires Prompt Variations
- Authors: Rem Hida, Masahiro Kaneko, Naoaki Okazaki,
- Abstract summary: Large Language Models (LLMs) exhibit considerable social biases.
This paper investigates the sensitivity of LLMs when changing prompt variations.
We show that LLMs have tradeoffs between performance and social bias caused by the prompts.
- Score: 38.91306092184724
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Warning: This paper contains examples of stereotypes and biases. Large Language Models (LLMs) exhibit considerable social biases, and various studies have tried to evaluate and mitigate these biases accurately. Previous studies use downstream tasks as prompts to examine the degree of social biases for evaluation and mitigation. While LLMs' output highly depends on prompts, previous studies evaluating and mitigating bias have often relied on a limited variety of prompts. In this paper, we investigate the sensitivity of LLMs when changing prompt variations (task instruction and prompt, few-shot examples, debias-prompt) by analyzing task performance and social bias of LLMs. Our experimental results reveal that LLMs are highly sensitive to prompts to the extent that the ranking of LLMs fluctuates when comparing models for task performance and social bias. Additionally, we show that LLMs have tradeoffs between performance and social bias caused by the prompts. Less bias from prompt setting may result in reduced performance. Moreover, the ambiguity of instances is one of the reasons for this sensitivity to prompts in advanced LLMs, leading to various outputs. We recommend using diverse prompts, as in this study, to compare the effects of prompts on social bias in LLMs.
Related papers
- Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility.
We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge.
Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
arXiv Detail & Related papers (2024-10-03T17:53:30Z) - Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions [25.809599403713506]
Large Language Models (LLMs) are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks.
LLMs are susceptible to societal biases due to their exposure to human-generated data.
This study investigates the presence of implicit gender biases in multi-agent LLM interactions and proposes two strategies to mitigate these biases.
arXiv Detail & Related papers (2024-10-03T15:28:05Z) - Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys [1.5727456947901746]
We conducted millions of simulations in which large language models (LLMs) were asked to answer subjective questions.
A comparison of different LLM responses with the European Social Survey (ESS) data suggests that the effect of prompts on bias and variability is fundamental.
arXiv Detail & Related papers (2024-05-29T17:54:22Z) - Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models.
We show that models give substantively different answers when not forced.
We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
arXiv Detail & Related papers (2024-02-26T18:00:49Z) - Exploring Value Biases: How LLMs Deviate Towards the Ideal [57.99044181599786]
Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact.
We show that value bias is strong in LLMs across different categories, similar to the results found in human studies.
arXiv Detail & Related papers (2024-02-16T18:28:43Z) - Do LLMs exhibit human-like response biases? A case study in survey
design [66.1850490474361]
We investigate the extent to which large language models (LLMs) reflect human response biases, if at all.
We design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires.
Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior.
arXiv Detail & Related papers (2023-11-07T15:40:43Z) - Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools.
Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions.
Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
arXiv Detail & Related papers (2023-09-20T09:23:46Z) - Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models [0.0]
This paper investigates bias along less-studied but still consequential, dimensions, such as age and beauty.
We ask whether LLMs hold wide-reaching biases of positive or negative sentiment for specific social groups similar to the "what is beautiful is good" bias found in people in experimental psychology.
arXiv Detail & Related papers (2023-09-16T07:07:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.