Auto-survey Challenge

TL;DR

This paper introduces a platform and competition for evaluating Large Language Models' ability to autonomously generate and critique survey papers across various disciplines, simulating peer review.

Contribution

It presents a novel evaluation framework for LLMs involving autonomous survey creation and critique, with a structured competition and assessment criteria.

Findings

Baseline models demonstrated varying levels of quality in survey generation.

Evaluation methods effectively measured clarity, references, and content value.

The platform enables benchmarking LLM capabilities in scholarly tasks.

Abstract

We present a novel platform for evaluating the capability of Large Language Models (LLMs) to autonomously compose and critique survey papers spanning a vast array of disciplines including sciences, humanities, education, and law. Within this framework, AI systems undertake a simulated peer-review mechanism akin to traditional scholarly journals, with human organizers serving in an editorial oversight capacity. Within this framework, we organized a competition for the AutoML conference 2023. Entrants are tasked with presenting stand-alone models adept at authoring articles from designated prompts and subsequently appraising them. Assessment criteria include clarity, reference appropriateness, accountability, and the substantive value of the content. This paper presents the design of the competition, including the implementation baseline submissions and methods of evaluation.