PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models

• URL: http://arxiv.org/abs/2402.13653v1
• Date: Wed, 21 Feb 2024 09:38:17 GMT
• Title: PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models
• Authors: Eli M Carrami and Sahand Sharifzadeh
• Abstract summary: We introduce the novel task of zero-shot Protein Question Answering (PQA) for free-form scientific enquiry. Given a previously unseen protein sequence and a natural language question, the task is to deliver a scientifically accurate answer. We contribute the first specialized dataset for PQA model training, containing 257K protein sequences annotated with 1.97M scientific question-answer pairs.
• Score: 5.062600294117055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the novel task of zero-shot Protein Question Answering (PQA) for free-form scientific enquiry. Given a previously unseen protein sequence and a natural language question, the task is to deliver a scientifically accurate answer. This task not only supports future biological research, but could also provide a test bed for assessing the scientific precision of large language models (LLMs). We contribute the first specialized dataset for PQA model training, containing 257K protein sequences annotated with 1.97M scientific question-answer pairs. Additionally, we propose and study several novel biologically relevant benchmarks for scientific PQA. Employing two robust multi-modal architectures, we establish an initial state-of-the-art performance for PQA and reveal key performance factors through ablation studies. Our comprehensive PQA framework, named Pika, including dataset, code, model checkpoints, and a user-friendly demo, is openly accessible on github.com/EMCarrami/Pika, promoting wider research and application in the field.

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