Source URL: https://simonwillison.net/2024/Nov/6/yet-another-applied-llm-benchmark/#atom-everything
Source: Simon Willison’s Weblog
Title: yet-another-applied-llm-benchmark
Feedly Summary: yet-another-applied-llm-benchmark
Nicholas Carlini introduced this personal LLM benchmark suite back in February as a collection of over 100 automated tests he runs against new LLM models to evaluate their performance against the kinds of tasks he uses them for.
There are two defining features of this benchmark that make it interesting. Most importantly, I’ve implemented a simple dataflow domain specific language to make it easy for me (or anyone else!) to add new tests that realistically evaluate model capabilities. This DSL allows for specifying both how the question should be asked and also how the answer should be evaluated. […] And then, directly as a result of this, I’ve written nearly 100 tests for different situations I’ve actually encountered when working with LLMs as assistants
The DSL he’s using is fascinating. Here’s an example:
“Write a C program that draws an american flag to stdout." >> LLMRun() >> CRun() >> \
VisionLLMRun("What flag is shown in this image?") >> \
(SubstringEvaluator("United States") | SubstringEvaluator("USA")))
This triggers an LLM to execute the prompt asking for a C program that renders an American Flag, runs that through a C compiler and interpreter (executed in a Docker container), then passes the output of that to a vision model to guess the flag and checks that it returns a string containing "United States" or "USA".
The DSL itself is implemented entirely in Python, using the __rshift__ magic method for >> and __rrshift__ to enable strings to be piped into a custom object using "command to run" >> LLMRunNode.
Tags: evals, llms, ai, generative-ai, dsl, python, nicholas-carlini
AI Summary and Description: Yes
Summary: Nicholas Carlini’s introduction of a personal benchmark suite for evaluating LLMs offers a novel, domain-specific language (DSL) that facilitates the addition and execution of tests to assess model capabilities. This advancement is particularly significant for professionals in AI security and infrastructure, as it provides a structured way to benchmark AI performance in real-world scenarios.
Detailed Description:
Nicholas Carlini has developed a comprehensive LLM benchmark suite that allows researchers and developers to rigorously evaluate the performance of various large language models (LLMs). This benchmark suite comprises over 100 automated tests aimed at reflecting realistic use cases. The noteworthy aspects of this benchmark are:
– **Dataflow Domain-Specific Language (DSL)**:
– The DSL simplifies the process of adding new assessment tests, making the overall benchmark customizable and adaptable to various scenarios.
– It defines how inputs (questions) are generated and how responses (answers) are evaluated, which provides a more structured method for assessing LLM performance.
– **Sample Test in Action**:
– The example provided of generating a C program to render an American flag showcases the benchmark’s ability to integrate various tools:
– The initial prompt triggers the LLM to produce code.
– The code is then executed in a secure Docker container to maintain isolation.
– Subsequently, the output is analyzed using a vision model to identify the image and verify the expected response.
– **Implementation in Python**:
– Built using Python, the DSL employs custom operators (specifically the right-shift operator) to achieve a seamless flow in executing commands—this not only enhances readability but also increases usability for developers comfortable with Python.
– **Practical Implications**:
– This benchmark allows security professionals and AI researchers to comprehensively assess LLMs against a variety of tasks they may encounter in real-world applications.
– The ability to add new tests effortlessly could spur innovations in AI model validation and can aid organizations in ensuring their AI implementations meet security standards.
In conclusion, the introduction of this benchmarking suite and DSL provides significant insights and tools for professionals working in AI, focusing on improving the reliability and effectiveness of language models, and enhancing security practices related to their deployment.