Measuring Bias, Toxicity, and Truthfulness in LLMs | 2min snip from The Real Python Podcast

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Measuring Bias, Toxicity, and Truthfulness in LLMs With Python

The Real Python Podcast

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CHAPTER

Measuring Bias, Toxicity, and Truthfulness in LLMs

This chapter explores the challenges of bias, toxicity, and misinformation in large language models (LLMs) and highlights the possibility of measuring these factors using Python packages.

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How can you measure the quality of a large language model? What tools can measure bias, toxicity, and truthfulness levels in a model using Python? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to discuss techniques and tools for evaluating LLMs With Python.

Jodie provides some background on large language models and how they can absorb vast amounts of information about the relationship between words using a type of neural network called a transformer. We discuss training datasets and the potential quality issues with crawling uncurated sources.

We dig into ways to measure levels of bias, toxicity, and hallucinations using Python. Jodie shares three benchmarking datasets and links to resources to get you started. We also discuss ways to augment models using agents or plugins, which can access search engine results or other authoritative sources.

This week’s episode is brought to you by Intel.

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