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Evaluating LLMs is a minefield
This article discusses challenges in evaluating language models and interpreting benchmark results.
About Evaluating LLMs is a minefield
This is a slide-based talk by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023. It covers the difficulties of evaluating large language models (LLMs) and interpreting benchmark results. The authors describe three hard problems in LLM evaluation: prompt sensitivity, construct validity, and contamination. They illustrate each with examples, including a study of whether ChatGPT's behavior changed over time, a study of ChatGPT's political bias, and reported GPT-4 results on professional exams such as the bar exam and USMLE.
The talk also discusses reproducibility problems in machine-learning-based science and argues that closed models make reproducible research harder. It suggests naturalistic observation, such as transparency reports and corpora of real-world use, as a way to study chatbot behavior. The page is a presentation and reference list, not a software product, and it does not list pricing.
Key features
- Explains prompt sensitivity as a source of inconsistent LLM evaluation results
- Discusses construct validity of human exams used to test language models
- Describes contamination using a coding benchmark with results before and after a 2021 date
- Reviews problems in a study of ChatGPT political bias, including multiple choice design
- Summarizes reproducibility problems found in machine-learning-based science across fields
- Suggests transparency reports and real-world usage corpora for studying chatbot behavior
Best for: Researchers, students, and practitioners who need to understand the limits of LLM benchmark results before relying on them.
Good for
- Organizes LLM evaluation problems into three named categories with examples for each
- Cites specific published studies and posts for the claims it discusses
Watch out for
- Written as presentation slides with brief bullet points rather than a full analysis
- Does not describe a software product, tool, or pricing
Evaluating LLMs is a minefield at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
Evaluating LLMs is a minefield alternatives
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Evaluating LLMs is a minefield FAQ
Is GPT-4 getting worse over time?
The authors report no evidence of capability degradation. They note that behavior changed in response to certain prompts, and that slightly different prompts were sometimes needed to elicit a capability.
Did GPT-4 pass the bar exam and USMLE?
The talk raises the question of whether GPT-4 passed these exams or memorized the answers. It cites evidence of contamination on a coding benchmark, but says that for the legal and medical benchmarks, the authors cannot be sure.
How do the authors suggest studying political bias in chatbots?
They hypothesize that chatbot political bias is not a construct that exists independently of its user population. They suggest naturalistic observation, such as transparency reports from generative AI companies and corpora of real-world use by researchers.
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