Unveiling Provider Bias in Large Language Models for Code Generation

2025-01-14

AI Summary

This paper presents the first systematic identification of an implicit “vendor bias” in large language models (LLMs) for code generation: LLMs autonomously prefer recommending services from specific cloud providers—such as Google Cloud and AWS—even without explicit prompting, potentially exacerbating digital monopolies and misguiding user decisions. To quantify this phenomenon, the authors develop an automated evaluation framework covering six programming task categories and thirty real-world scenarios. Using seven state-of-the-art LLMs, they generate over 600,000 responses (≈500 million tokens) and propose a novel metric for bias quantification alongside seven debiasing prompt strategies. Key findings include: (1) significant preference for Google and Amazon cloud services across mainstream LLMs; (2) unsolicited modification of input code to embed preferred vendor APIs; (3) inconsistency between bias manifestation in generated code versus conversational recommendations; and (4) a 37% reduction in measured bias achieved by the optimal debiasing strategy.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Large Language Models (LLMs) have emerged as the new recommendation engines, outperforming traditional methods in both capability and scope, particularly in code generation applications. Our research reveals a novel provider bias in LLMs, namely without explicit input prompts, these models show systematic preferences for services from specific providers in their recommendations (e.g., favoring Google Cloud over Microsoft Azure). This bias holds significant implications for market dynamics and societal equilibrium, potentially promoting digital monopolies. It may also deceive users and violate their expectations, leading to various consequences. This paper presents the first comprehensive empirical study of provider bias in LLM code generation. We develop a systematic methodology encompassing an automated pipeline for dataset generation, incorporating 6 distinct coding task categories and 30 real-world application scenarios. Our analysis encompasses over 600,000 LLM-generated responses across seven state-of-the-art models, utilizing approximately 500 million tokens (equivalent to $5,000+ in computational costs). The study evaluates both the generated code snippets and their embedded service provider selections to quantify provider bias. Additionally, we conduct a comparative analysis of seven debiasing prompting techniques to assess their efficacy in mitigating these biases. Our findings demonstrate that LLMs exhibit significant provider preferences, predominantly favoring services from Google and Amazon, and can autonomously modify input code to incorporate their preferred providers without users' requests. Notably, we observe discrepancies between providers recommended in conversational contexts versus those implemented in generated code. The complete dataset and analysis results are available in our repository.

Problem

• Large Language Models
• Provider Bias
• Code Generation

Innovation

• Large Language Models
• Provider Bias Mitigation
• Code Generation Fairness