Summary of Human-calibrated Automated Testing and Validation Of Generative Language Models, by Agus Sudjianto et al.
Human-Calibrated Automated Testing and Validation of Generative Language Models
by Agus Sudjianto, Aijun Zhang, Srinivas Neppalli, Tarun Joshi, Michal Malohlava
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a comprehensive framework for evaluating generative language models (GLMs), specifically Retrieval-Augmented Generation (RAG) systems used in high-stakes domains like banking. The authors address the challenge of assessing GLM quality, which is difficult due to open-ended outputs and subjective evaluations. By leveraging RAG’s structured nature, they introduce Human-Calibrated Automated Testing (HCAT), combining automated test generation, embedding-based metrics for explainable assessment, and a two-stage calibration approach that aligns machine-generated evaluations with human judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better language models by creating a new way to test them. Language models are hard to evaluate because they can generate lots of different answers and people have different opinions on what’s good or bad. To fix this, the authors created a testing framework called HCAT that uses computers and humans together. It has three parts: first, it generates tests using a special method; second, it uses math to understand how well the model is doing in certain areas like safety; and third, it makes sure machines and people agree on what’s good or bad. |
Keywords
» Artificial intelligence » Embedding » Rag » Retrieval augmented generation
