mlsanityData to 14 Oct 2026

A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications

arXiv 2310.17750 ↗First posted 26 Oct 2023cs.CLcited by 12

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Abstract (arXiv)

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 3 of its 4 references that are in the map. First posted on arXiv 26 Oct 2023.

Builds on

3 of 4 references.

References · 3

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 763
  3. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 4; 2026: 1.

12 papers.