Measuring Bias, Toxicity, and Truthfulness in LLMs With Python

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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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Topics:

00:00:00 – Introduction
00:02:19 – Testing characteristics of LLMs with Python
00:04:18 – Background on LLMs
00:08:35 – Training of models
00:14:23 – Uncurated sources of training
00:16:12 – Safeguards and prompt engineering
00:21:19 – TruthfulQA and creating a more strict prompt
00:23:20 – Information that is out of date
00:26:07 – WinoBias for evaluating gender stereotypes
00:28:30 – BOLD dataset for evaluating bias
00:30:28 – Sponsor: Intel
00:31:18 – Using Hugging Face to start testing with Python
00:35:25 – Using the transformers package
00:37:34 – Using langchain for proprietary models
00:43:04 – Putting the tools together and evaluating
00:47:19 – Video Course Spotlight
00:48:29 – Assessing toxicity
00:50:21 – Measuring bias
00:54:40 – Checking the hallucination rate
00:56:22 – LLM leaderboards
00:58:17 – What helped ChatGPT leap forward?
01:06:01 – Improvements of what is being crawled
01:07:32 – Revisiting agents and RAG
01:11:03 – ChatGPT plugins and Wolfram-Alpha
01:13:06 – How can people follow your work online?
01:14:33 – Thanks and goodbye

Background Links:

Dataset Links:

Tutorials and Documentation for Python Packages:

Measurement Links:

Training Data for LLMs:

Agents and Plugin Links:

Additional Links:

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