How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies

• URL: http://arxiv.org/abs/2407.11733v2
• Date: Thu, 1 Aug 2024 15:09:12 GMT
• Title: How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies
• Authors: Alina Leidinger, Richard Rogers,
• Abstract summary: Commercial model development has focused efforts on'safety' training concerning legal liabilities at the expense of social impact evaluation. This mimics a similar trend which we could observe for search engine autocompletion some years prior. We present a novel evaluation task in the style of autocompletion prompts to assess stereotyping in LLMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the widespread availability of LLMs since the release of ChatGPT and increased public scrutiny, commercial model development appears to have focused their efforts on 'safety' training concerning legal liabilities at the expense of social impact evaluation. This mimics a similar trend which we could observe for search engine autocompletion some years prior. We draw on scholarship from NLP and search engine auditing and present a novel evaluation task in the style of autocompletion prompts to assess stereotyping in LLMs. We assess LLMs by using four metrics, namely refusal rates, toxicity, sentiment and regard, with and without safety system prompts. Our findings indicate an improvement to stereotyping outputs with the system prompt, but overall a lack of attention by LLMs under study to certain harms classified as toxic, particularly for prompts about peoples/ethnicities and sexual orientation. Mentions of intersectional identities trigger a disproportionate amount of stereotyping. Finally, we discuss the implications of these findings about stereotyping harms in light of the coming intermingling of LLMs and search and the choice of stereotyping mitigation policy to adopt. We address model builders, academics, NLP practitioners and policy makers, calling for accountability and awareness concerning stereotyping harms, be it for training data curation, leader board design and usage, or social impact measurement.

Related papers

• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions [3.1247504290622214]
  Research has raised concerns about the potential for Large Language Models to produce discriminatory outcomes and unsafe behaviors in real-world robot experiments and applications. We conduct an HRI-based evaluation of discrimination and safety criteria on several highly-rated LLMs. Our results underscore the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes.
  arXiv  Detail & Related papers  (2024-06-13T05:31:49Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations [15.535416139394009]
  Large language models (LLMs) have emerged as an integral part of modern societies. Despite their utility, research indicates that LLMs perpetuate systemic biases. We introduce the Covert Harms and Social Threats (CHAST), a set of seven metrics grounded in social science literature.
  arXiv  Detail & Related papers  (2024-05-08T19:08:45Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming [64.86326523181553]
  ALERT is a large-scale benchmark to assess safety based on a novel fine-grained risk taxonomy. It aims to identify vulnerabilities, inform improvements, and enhance the overall safety of the language models.
  arXiv  Detail & Related papers  (2024-04-06T15:01:47Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Prejudice and Volatility: A Statistical Framework for Measuring Social Discrimination in Large Language Models [0.0]
  This study investigates why and how inconsistency in the generation of Large Language Models (LLMs) might induce or exacerbate societal injustice. We formulate the Prejudice-Volatility Framework (PVF) that precisely defines behavioral metrics for assessing LLMs. We mathematically dissect the aggregated discrimination risk of LLMs into prejudice risk, originating from their system bias, and volatility risk.
  arXiv  Detail & Related papers  (2024-02-23T18:15:56Z)
• StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large Language Models [11.218531873222398]
  Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society.
  arXiv  Detail & Related papers  (2023-10-20T17:22:30Z)
• Safety Assessment of Chinese Large Language Models [51.83369778259149]
  Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes. To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
  arXiv  Detail & Related papers  (2023-04-20T16:27:35Z)

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.