Inducing anxiety in large language models can induce bias
- URL: http://arxiv.org/abs/2304.11111v2
- Date: Tue, 15 Oct 2024 14:20:51 GMT
- Title: Inducing anxiety in large language models can induce bias
- Authors: Julian Coda-Forno, Kristin Witte, Akshay K. Jagadish, Marcel Binz, Zeynep Akata, Eric Schulz,
- Abstract summary: We focus on twelve established large language models (LLMs) and subject them to a questionnaire commonly used in psychiatry.
Our results show that six of the latest LLMs respond robustly to the anxiety questionnaire, producing comparable anxiety scores to humans.
Anxiety-induction not only influences LLMs' scores on an anxiety questionnaire but also influences their behavior in a previously-established benchmark measuring biases such as racism and ageism.
- Score: 47.85323153767388
- License:
- Abstract: Large language models (LLMs) are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also why they fail and misbehave is of great societal relevance. We propose to turn the lens of psychiatry, a framework used to describe and modify maladaptive behavior, to the outputs produced by these models. We focus on twelve established LLMs and subject them to a questionnaire commonly used in psychiatry. Our results show that six of the latest LLMs respond robustly to the anxiety questionnaire, producing comparable anxiety scores to humans. Moreover, the LLMs' responses can be predictably changed by using anxiety-inducing prompts. Anxiety-induction not only influences LLMs' scores on an anxiety questionnaire but also influences their behavior in a previously-established benchmark measuring biases such as racism and ageism. Importantly, greater anxiety-inducing text leads to stronger increases in biases, suggesting that how anxiously a prompt is communicated to large language models has a strong influence on their behavior in applied settings. These results demonstrate the usefulness of methods taken from psychiatry for studying the capable algorithms to which we increasingly delegate authority and autonomy.
Related papers
- Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models [0.0]
Large language models (LLMs) are increasingly effective at persuading us that their answers are valuable.
This study examines what are the psycholinguistic features of the responses used by twelve different language models.
We ask whether and how we can mitigate the risks of LLM-driven mass misinformation.
arXiv Detail & Related papers (2025-02-13T15:15:53Z) - CBEval: A framework for evaluating and interpreting cognitive biases in LLMs [1.4633779950109127]
Large Language models exhibit notable gaps in their cognitive processes.
As reflections of human-generated data, these models have the potential to inherit cognitive biases.
arXiv Detail & Related papers (2024-12-04T05:53:28Z) - Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments [24.15688619889342]
Large Language Models (LLMs) are trained on large corpora written by humans and demonstrate high performance on various tasks.
As humans are susceptible to cognitive biases, LLMs can also be influenced by these biases, leading to irrational decision-making.
arXiv Detail & Related papers (2024-11-30T02:37:59Z) - 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) - Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants.
This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation.
We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
arXiv Detail & Related papers (2024-06-25T16:09:08Z) - "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust [51.542856739181474]
We show how different natural language expressions of uncertainty impact participants' reliance, trust, and overall task performance.
We find that first-person expressions decrease participants' confidence in the system and tendency to agree with the system's answers, while increasing participants' accuracy.
Our findings suggest that using natural language expressions of uncertainty may be an effective approach for reducing overreliance on LLMs, but that the precise language used matters.
arXiv Detail & Related papers (2024-05-01T16:43:55Z) - Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided [38.11184388388781]
Large language models (LLMs) have offered new opportunities for emotional support.
This work takes a first step by engaging with cognitive reappraisals.
We conduct a first-of-its-kind expert evaluation of an LLM's zero-shot ability to generate cognitive reappraisal responses.
arXiv Detail & Related papers (2024-04-01T17:56:30Z) - Fine-Grained Self-Endorsement Improves Factuality and Reasoning [72.83651220132495]
This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations.
We propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses.
arXiv Detail & Related papers (2024-02-23T22:24:40Z) - 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) - The Confidence-Competence Gap in Large Language Models: A Cognitive
Study [3.757390057317548]
Large Language Models (LLMs) have acquired ubiquitous attention for their performances across diverse domains.
We exploit these models with diverse sets of questionnaires and real-world scenarios.
Our findings reveal intriguing instances where models demonstrate high confidence even when they answer incorrectly.
arXiv Detail & Related papers (2023-09-28T03:50:09Z)
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.