SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking

TL;DR

SoAy introduces a solution-based methodology using pre-constructed API calling sequences to enhance large language models' ability to handle complex academic API queries, significantly improving performance.

Contribution

It presents a novel solution-based approach with a benchmark and evaluation framework, addressing API coupling challenges in academic information seeking with LLMs.

Findings

Achieves 34.58-75.99% performance improvement over baselines.

Introduces SoAyBench and SoAyEval for systematic evaluation.

Provides publicly accessible datasets, code, and models.

Abstract

Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods struggle with complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. The addition of the solution reduces the difficulty for the model to understand the complex relationships between APIs. Code improves the efficiency of reasoning. To evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99\% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://github.com/RUCKBReasoning/SoAy.