5 items tagged “evals”
Evaluations and benchmarks of AI systems, for example to compare different models and prompting strategies.
2024
LLMs are bad at returning code in JSON (via) Paul Gauthier's Aider is a terminal-based coding assistant which works against multiple different models. As part of developing the project Paul runs extensive benchmarks, and his latest shows an interesting result: LLMs are slightly less reliable at producing working code if you request that code be returned as part of a JSON response.
The May release of GPT-4o is the closest to a perfect score - the August appears to have regressed slightly, and the new structured output mode doesn't help and could even make things worse (though that difference may not be statistically significant).
Paul recommends using Markdown delimiters here instead, which are less likely to introduce confusing nested quoting issues.
Claude: Building evals and test cases. More documentation updates from Anthropic: this section on writing evals for Claude is new today and includes Python code examples for a number of different evaluation techniques.
Included are several examples of the LLM-as-judge pattern, plus an example using cosine similarity and another that uses the new-to-me Rouge Python library that implements the ROUGE metric for evaluating the quality of summarized text.
What We Learned from a Year of Building with LLMs (Part I). Accumulated wisdom from six experienced LLM hackers. Lots of useful tips in here. On providing examples in a prompt:
If n is too low, the model may over-anchor on those specific examples, hurting its ability to generalize. As a rule of thumb, aim for n ≥ 5. Don’t be afraid to go as high as a few dozen.
There's a recommendation not to overlook keyword search when implementing RAG - tricks with embeddings can miss results for things like names or acronyms, and keyword search is much easier to debug.
Plus this tip on using the LLM-as-judge pattern for implementing automated evals:
Instead of asking the LLM to score a single output on a Likert scale, present it with two options and ask it to select the better one. This tends to lead to more stable results.
Weeknotes: Llama 3, AI for Data Journalism, llm-evals and datasette-secrets
Llama 3 landed on Thursday. I ended up updating a whole bunch of different plugins to work with it, described in Options for accessing Llama 3 from the terminal using LLM.
[... 1,030 words]Your AI Product Needs Evals (via) Hamel Husain: “I’ve seen many successful and unsuccessful approaches to building LLM products. I’ve found that unsuccessful products almost always share a common root cause: a failure to create robust evaluation systems.”
I’ve been frustrated about this for a while: I know I need to move beyond “vibe checks” for the systems I have started to build on top of LLMs, but I was lacking a thorough guide about how to build automated (and manual) evals in a productive way.
Hamel has provided exactly the tutorial I was needing for this, with a really thorough example case-study.
Using GPT-4 to create test cases is an interesting approach: “Write 50 different instructions that a real estate agent can give to his assistant to create contacts on his CRM. The contact details can include name, phone, email, partner name, birthday, tags, company, address and job.”
Also important: “... unlike traditional unit tests, you don’t necessarily need a 100% pass rate. Your pass rate is a product decision.”
Hamel’s guide then covers the importance of traces for evaluating real-world performance of your deployed application, plus the pros and cons of leaning on automated evaluation using LLMs themselves.
Plus some wisdom from a footnote: “A reasonable heuristic is to keep reading logs until you feel like you aren’t learning anything new.”