A Survey on Fairness in Large Language Models
- URL: http://arxiv.org/abs/2308.10149v2
- Date: Wed, 21 Feb 2024 13:52:11 GMT
- Title: A Survey on Fairness in Large Language Models
- Authors: Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang
- Abstract summary: Large Language Models (LLMs) have shown powerful performance and development prospects.
LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks.
Unfair LLM systems have undesirable social impacts and potential harms.
- Score: 28.05516809190299
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have shown powerful performance and development
prospects and are widely deployed in the real world. However, LLMs can capture
social biases from unprocessed training data and propagate the biases to
downstream tasks. Unfair LLM systems have undesirable social impacts and
potential harms. In this paper, we provide a comprehensive review of related
research on fairness in LLMs. Considering the influence of parameter magnitude
and training paradigm on research strategy, we divide existing fairness
research into oriented to medium-sized LLMs under pre-training and fine-tuning
paradigms and oriented to large-sized LLMs under prompting paradigms. First,
for medium-sized LLMs, we introduce evaluation metrics and debiasing methods
from the perspectives of intrinsic bias and extrinsic bias, respectively. Then,
for large-sized LLMs, we introduce recent fairness research, including fairness
evaluation, reasons for bias, and debiasing methods. Finally, we discuss and
provide insight on the challenges and future directions for the development of
fairness in LLMs.
Related papers
- Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers [27.66626125248612]
This paper presents an empirical study evaluating Large Language Models (LLMs) using the TREC Fair Ranking dataset.
We focus on the representation of binary protected attributes such as gender and geographic location, which are historically underrepresented in search outcomes.
Our analysis delves into how these LLMs handle queries and documents related to these attributes, aiming to uncover biases in their ranking algorithms.
arXiv Detail & Related papers (2024-04-04T04:23:19Z) - Fairness in Large Language Models: A Taxonomic Survey [2.669847575321326]
Large Language Models (LLMs) have demonstrated remarkable success across various domains.
Despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations.
arXiv Detail & Related papers (2024-03-31T22:22:53Z) - Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing
Framework [20.753141804841]
Large language models (LLMs) can easily generate biased and discriminative responses.
This paper focuses on social bias, tackling the association between demographic information and LLM outputs.
arXiv Detail & Related papers (2024-03-13T17:46:28Z) - 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) - Large Language Models: A Survey [69.72787936480394]
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks.
LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
arXiv Detail & Related papers (2024-02-09T05:37:09Z) - A Group Fairness Lens for Large Language Models [34.0579082699443]
Large language models can perpetuate biases and unfairness when deployed in social media contexts.
We propose evaluating LLM biases from a group fairness lens using a novel hierarchical schema characterizing diverse social groups.
We pioneer a novel chain-of-thought method GF-Think to mitigate biases of LLMs from a group fairness perspective.
arXiv Detail & Related papers (2023-12-24T13:25:15Z) - Selecting Shots for Demographic Fairness in Few-Shot Learning with Large
Language Models [14.772568847965408]
We explore the effect of shots, which directly affect the performance of models, on the fairness of large language models (LLMs) as NLP classification systems.
We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets.
arXiv Detail & Related papers (2023-11-14T19:02:03Z) - Survey on Factuality in Large Language Models: Knowledge, Retrieval and
Domain-Specificity [61.54815512469125]
This survey addresses the crucial issue of factuality in Large Language Models (LLMs)
As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
arXiv Detail & Related papers (2023-10-11T14:18:03Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - Aligning Large Language Models with Human: A Survey [53.6014921995006]
Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks.
Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information.
This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
arXiv Detail & Related papers (2023-07-24T17:44:58Z) - On Learning to Summarize with Large Language Models as References [101.79795027550959]
Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets.
We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
arXiv Detail & Related papers (2023-05-23T16:56:04Z)
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.