Bias Similarity Across Large Language Models
- URL: http://arxiv.org/abs/2410.12010v2
- Date: Wed, 19 Feb 2025 15:36:26 GMT
- Title: Bias Similarity Across Large Language Models
- Authors: Hyejun Jeong, Shiqing Ma, Amir Houmansadr,
- Abstract summary: We analyze bias through output distribution across multiple dimensions using two datasets (4K and 1M questions)
Our results show that fine-tuning has minimal impact on output distributions, and proprietary models tend to overly response as unknowns to minimize bias, compromising accuracy and utility.
Open-source models like Llama3-Chat and Gemma2-it demonstrate fairness comparable to proprietary models like GPT-4, challenging the assumption that larger, closed-source models are inherently less biased.
- Score: 32.0365189539138
- License:
- Abstract: Bias in machine learning models, particularly in Large Language Models, is a critical issue as these systems shape important societal decisions. While previous studies have examined bias in individual LLMs, comparisons of bias across models remain underexplored. To address this gap, we analyze 13 LLMs from five families, evaluating bias through output distribution across multiple dimensions using two datasets (4K and 1M questions). Our results show that fine-tuning has minimal impact on output distributions, and proprietary models tend to overly response as unknowns to minimize bias, compromising accuracy and utility. In addition, open-source models like Llama3-Chat and Gemma2-it demonstrate fairness comparable to proprietary models like GPT-4, challenging the assumption that larger, closed-source models are inherently less biased. We also find that bias scores for disambiguated questions are more extreme, raising concerns about reverse discrimination. These findings highlight the need for improved bias mitigation strategies and more comprehensive evaluation metrics for fairness in LLMs.
Related papers
- Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models [1.787433808079955]
Large language models (LLMs) have been observed to perpetuate unwanted biases in training data.
In this paper, we mitigate bias by leveraging small biased and anti-biased expert models to obtain a debiasing signal.
Experiments on mitigating gender, race, and religion biases show a reduction in bias on several local and global bias metrics.
arXiv Detail & Related papers (2024-12-02T16:56:08Z) - How far can bias go? -- Tracing bias from pretraining data to alignment [54.51310112013655]
This study examines the correlation between gender-occupation bias in pre-training data and their manifestation in LLMs.
Our findings reveal that biases present in pre-training data are amplified in model outputs.
arXiv Detail & Related papers (2024-11-28T16:20:25Z) - Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs [0.0]
Large Language Models (LLMs) are being adopted across a wide range of tasks.
Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations.
This study highlights that newer or larger language models do not automatically exhibit reduced bias.
arXiv Detail & Related papers (2024-10-13T03:43:18Z) - REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning [18.064064773660174]
We introduce REFINE-LM, a debiasing method that uses reinforcement learning to handle different types of biases without any fine-tuning.
By training a simple model on top of the word probability distribution of a LM, our bias reinforcement learning method enables model debiasing without human annotations.
Experiments conducted on a wide range of models, including several LMs, show that our method significantly reduces stereotypical biases while preserving LMs performance.
arXiv Detail & Related papers (2024-08-18T14:08:31Z) - BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization [0.0]
Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns.
This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in English text.
By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language.
arXiv Detail & Related papers (2024-07-18T22:32:20Z) - VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model [72.13121434085116]
We introduce VLBiasBench, a benchmark to evaluate biases in Large Vision-Language Models (LVLMs)
VLBiasBench features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status.
We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models.
arXiv Detail & Related papers (2024-06-20T10:56:59Z) - Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models [56.02275285521847]
We propose to evaluate models using a Panel of LLm evaluators (PoLL)
We find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive.
arXiv Detail & Related papers (2024-04-29T15:33:23Z) - ROBBIE: Robust Bias Evaluation of Large Generative Language Models [27.864027322486375]
Different prompt-based datasets can be used to measure social bias across multiple text domains and demographic axes.
We compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
We conduct a comprehensive study of how well 3 bias/toxicity mitigation techniques perform across our suite of measurements.
arXiv Detail & Related papers (2023-11-29T23:03:04Z) - Fast Model Debias with Machine Unlearning [54.32026474971696]
Deep neural networks might behave in a biased manner in many real-world scenarios.
Existing debiasing methods suffer from high costs in bias labeling or model re-training.
We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
arXiv Detail & Related papers (2023-10-19T08:10:57Z) - General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space.
GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z) - Learning from others' mistakes: Avoiding dataset biases without modeling
them [111.17078939377313]
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task.
Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available.
We show a method for training models that learn to ignore these problematic correlations.
arXiv Detail & Related papers (2020-12-02T16:10:54Z)
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.