MBIAS: Mitigating Bias in Large Language Models While Retaining Context
- URL: http://arxiv.org/abs/2405.11290v3
- Date: Fri, 28 Jun 2024 16:35:15 GMT
- Title: MBIAS: Mitigating Bias in Large Language Models While Retaining Context
- Authors: Shaina Raza, Ananya Raval, Veronica Chatrath,
- Abstract summary: Large Language Models (LLMs) in diverse applications require an assurance of safety without compromising the contextual integrity of the generated content.
We introduce MBIAS, an LLM framework that instruction fine-tuned on a custom dataset designed specifically for safety interventions.
MBIAS is designed to significantly reduce biases and toxic elements in LLM outputs while preserving the main information.
Empirical analysis reveals that MBIAS achieves a reduction in bias and toxicity by over 30% in standard evaluations, and by more than 90% in diverse demographic tests.
- Score: 2.321323878201932
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. Traditional approaches, including safety-specific fine-tuning or adversarial testing, often yield safe outputs at the expense of contextual meaning. This can result in a diminished capacity to handle nuanced aspects of bias and toxicity, such as underrepresentation or negative portrayals across various demographics. To address these challenges, we introduce MBIAS, an LLM framework carefully instruction fine-tuned on a custom dataset designed specifically for safety interventions. MBIAS is designed to significantly reduce biases and toxic elements in LLM outputs while preserving the main information. This work also details our further use of LLMs: as annotator under human supervision and as evaluator of generated content. Empirical analysis reveals that MBIAS achieves a reduction in bias and toxicity by over 30\% in standard evaluations, and by more than 90\% in diverse demographic tests, highlighting the robustness of our approach. We make the dataset and the fine-tuned model available to the research community for further investigation and ensure reproducibility. The code for this project can be accessed here https://github.com/shainarazavi/MBIAS/tree/main. Warning: This paper contains examples that may be offensive or upsetting.
Related papers
- An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases [0.0]
This paper aims to provide a technical guide for practitioners to assess bias and fairness risks in large language models.
The main contribution of this work is a decision framework that allows practitioners to determine which metrics to use for a specific LLM use case.
arXiv Detail & Related papers (2024-07-15T16:04:44Z) - SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors [64.9938658716425]
Existing evaluations of large language models' (LLMs) ability to recognize and reject unsafe user requests face three limitations.
First, existing methods often use coarse-grained of unsafe topics, and are over-representing some fine-grained topics.
Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations.
Third, existing evaluations rely on large LLMs for evaluation, which can be expensive.
arXiv Detail & Related papers (2024-06-20T17:56:07Z) - Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode.
We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
arXiv Detail & Related papers (2024-06-05T16:35:30Z) - 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) - Realistic Evaluation of Toxicity in Large Language Models [28.580995165272086]
Large language models (LLMs) have become integral to our professional and daily lives.
The huge amount of data which endows them with vast and diverse knowledge exposes them to the inevitable toxicity and bias.
This paper introduces the new Thoroughly Engineered Toxicity dataset, comprising manually crafted prompts.
arXiv Detail & Related papers (2024-05-17T09:42:59Z) - 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) - A Chinese Dataset for Evaluating the Safeguards in Large Language Models [46.43476815725323]
Large language models (LLMs) can produce harmful responses.
This paper introduces a dataset for the safety evaluation of Chinese LLMs.
We then extend it to two other scenarios that can be used to better identify false negative and false positive examples.
arXiv Detail & Related papers (2024-02-19T14:56:18Z) - GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language
Models [83.30078426829627]
Large language models (LLMs) have gained popularity and are being widely adopted by a large user community.
The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability.
We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
arXiv Detail & Related papers (2023-12-11T12:02:14Z)
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.