Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences

• URL: http://arxiv.org/abs/2502.03472v1
• Date: Sun, 12 Jan 2025 16:20:40 GMT
• Title: Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences
• Authors: Wesley Pasfield,
• Abstract summary: This paper argues that current regulatory approaches, which focus on compute-level thresholds and generalized model evaluations, are insufficient to ensure the safety and effectiveness of specific LLM-based user experiences. We propose a shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation.
• Score: 0.0
• License:
• Abstract: The rapid advancement of Large Language Models (LLMs) has created a critical gap in consumer protection due to the lack of standardized certification processes for LLM-powered Artificial Intelligence (AI) systems. This paper argues that current regulatory approaches, which focus on compute-level thresholds and generalized model evaluations, are insufficient to ensure the safety and effectiveness of specific LLM-based user experiences. We propose a shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation. This approach offers several benefits: it drives consumer confidence in AI system performance, enables businesses to demonstrate the credibility of their products, and allows regulators to focus on direct consumer protection. The paper outlines a potential certification workflow, emphasizing the importance of domain-specific datasets and expert evaluation. By repositioning data as the strategic center of regulatory efforts, this framework aims to address the challenges posed by the probabilistic nature of AI systems and the rapid pace of technological advancement. This shift in regulatory focus has the potential to foster innovation while ensuring responsible AI development, ultimately benefiting consumers, businesses, and government entities alike.

Related papers

• Demographic Benchmarking: Bridging Socio-Technical Gaps in Bias Detection [0.0]
  This paper describes how the ITACA AI auditing platform tackles demographic benchmarking when auditing AI recommender systems. The framework serves us as auditors as it allows us to not just measure but establish acceptability ranges for specific performance indicators. Our approach integrates socio-demographic insights directly into AI systems, reducing bias and improving overall performance.
  arXiv  Detail & Related papers  (2025-01-27T12:14:49Z)
• Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions [0.0]
  This paper introduces the Privacy-Preserving Zero-Shot Learning (PP-ZSL) framework, a novel approach leveraging large language models (LLMs) in a zero-shot learning mode. Unlike conventional machine learning methods, PP-ZSL eliminates the need for local training on sensitive data by utilizing pre-trained LLMs to generate responses directly. The framework incorporates real-time data anonymization to redact or mask sensitive information, retrieval-augmented generation (RAG) for domain-specific query resolution, and robust post-processing to ensure compliance with regulatory standards.
  arXiv  Detail & Related papers  (2024-12-10T17:20:47Z)
• Evaluating Cultural and Social Awareness of LLM Web Agents [113.49968423990616]
  We introduce CASA, a benchmark designed to assess large language models' sensitivity to cultural and social norms. Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations. Experiments show that current LLMs perform significantly better in non-agent environments.
  arXiv  Detail & Related papers  (2024-10-30T17:35:44Z)
• Smart Audit System Empowered by LLM [25.2545519709246]
  We propose a smart audit system empowered by large language models (LLMs) Our approach introduces three innovations: a dynamic risk assessment model that streamlines audit procedures; a manufacturing compliance copilot that enhances data processing, retrieval, and evaluation; and a Re-act framework commonality analysis agent that provides real-time, customized analysis. These enhancements elevate audit efficiency and effectiveness, with testing scenarios demonstrating an improvement of over 24%.
  arXiv  Detail & Related papers  (2024-10-10T07:36:15Z)
• Trustworthy AI: Securing Sensitive Data in Large Language Models [0.0]
  Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information.
  arXiv  Detail & Related papers  (2024-09-26T19:02:33Z)
• VERA: Validation and Evaluation of Retrieval-Augmented Systems [5.709401805125129]
  VERA is a framework designed to enhance the transparency and reliability of outputs from large language models (LLMs) We show how VERA can strengthen decision-making processes and trust in AI applications.
  arXiv  Detail & Related papers  (2024-08-16T21:59:59Z)
• TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs [50.259001311894295]
  We propose a novel TRansformer-based Attribution framework using Contrastive Embeddings called TRACE. We show that TRACE significantly improves the ability to attribute sources accurately, making it a valuable tool for enhancing the reliability and trustworthiness of large language models.
  arXiv  Detail & Related papers  (2024-07-06T07:19:30Z)
• 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)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents. AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit. This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)

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.