Acceleron: A Tool to Accelerate Research Ideation

• URL: http://arxiv.org/abs/2403.04382v1
• Date: Thu, 7 Mar 2024 10:20:06 GMT
• Title: Acceleron: A Tool to Accelerate Research Ideation
• Authors: Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff
• Abstract summary: Acceleron is a research accelerator for different phases of the research life cycle. It guides researchers through the formulation of a comprehensive research proposal, encompassing a novel research problem. We leverage the reasoning and domain-specific skills of Large Language Models (LLMs) to create an agent-based architecture.
• Score: 15.578814192003437
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Several tools have recently been proposed for assisting researchers during various stages of the research life-cycle. However, these primarily concentrate on tasks such as retrieving and recommending relevant literature, reviewing and critiquing the draft, and writing of research manuscripts. Our investigation reveals a significant gap in availability of tools specifically designed to assist researchers during the challenging ideation phase of the research life-cycle. To aid with research ideation, we propose `Acceleron', a research accelerator for different phases of the research life cycle, and which is specially designed to aid the ideation process. Acceleron guides researchers through the formulation of a comprehensive research proposal, encompassing a novel research problem. The proposals motivation is validated for novelty by identifying gaps in the existing literature and suggesting a plausible list of techniques to solve the proposed problem. We leverage the reasoning and domain-specific skills of Large Language Models (LLMs) to create an agent-based architecture incorporating colleague and mentor personas for LLMs. The LLM agents emulate the ideation process undertaken by researchers, engaging researchers in an interactive fashion to aid in the development of the research proposal. Notably, our tool addresses challenges inherent in LLMs, such as hallucinations, implements a two-stage aspect-based retrieval to manage precision-recall trade-offs, and tackles issues of unanswerability. As evaluation, we illustrate the execution of our motivation validation and method synthesis workflows on proposals from the ML and NLP domain, given by 3 distinct researchers. Our observations and evaluations provided by the researchers illustrate the efficacy of the tool in terms of assisting researchers with appropriate inputs at distinct stages and thus leading to improved time efficiency.

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