BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology

TL;DR

This paper introduces BioPlanner, an automatic evaluation framework for LLMs in biology protocol planning, utilizing pseudocode representations to assess and improve their multi-step scientific reasoning capabilities.

Contribution

It presents BioProt, a novel dataset of biology protocols with pseudocode, and a framework for evaluating LLMs' ability to generate and reconstruct scientific protocols.

Findings

GPT-4 outperforms GPT-3 in protocol reconstruction

Generated protocols were successfully executed in a biological lab

Pseudocode improves evaluation and generation of scientific protocols

Abstract

The ability to automatically generate accurate protocols for scientific experiments would represent a major step towards the automation of science. Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and code. However, LLMs can struggle with multi-step problems and long-term planning, which are crucial for designing scientific experiments. Moreover, evaluation of the accuracy of scientific protocols is challenging, because experiments can be described correctly in many different ways, require expert knowledge to evaluate, and cannot usually be executed automatically. Here we present an automatic evaluation framework for the task of planning experimental protocols, and we introduce BioProt: a dataset of biology protocols with corresponding pseudocode representations. To measure performance on generating scientific protocols, we use an LLM to convert a natural language protocol into pseudocode, and then evaluate an LLM's ability to reconstruct the pseudocode from a high-level description and a list of admissible pseudocode functions. We evaluate GPT-3 and GPT-4 on this task and explore their robustness. We externally validate the utility of pseudocode representations of text by generating accurate novel protocols using retrieved pseudocode, and we run a generated protocol successfully in our biological laboratory. Our framework is extensible to the evaluation and improvement of language model planning abilities in other areas of science or other areas that lack automatic evaluation.