@inproceedings{nigam-etal-2024-interactive,
title = "An Interactive Co-Pilot for Accelerated Research Ideation",
author = "Nigam, Harshit and
Patwardhan, Manasi and
Vig, Lovekesh and
Shroff, Gautam",
editor = "Blodgett, Su Lin and
Curry, Amanda Cercas and
Dey, Sunipa and
Madaio, Michael and
Nenkova, Ani and
Yang, Diyi and
Xiao, Ziang",
booktitle = "Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.hcinlp-1.6",
pages = "60--73",
abstract = "In the realm of research support tools, there exists a notable void in resources tailored specifically for aiding researchers during the crucial ideation phase of the research life-cycle. We address this gap by introducing {`}Acceleron{'}, a {`}Co-Pilot{'} for researchers, designed specifically to accelerate the ideation phase of the research life-cycle. Leveraging the reasoning and domain-specific skills of Large Language Models (LLMs) within an agent-based architecture with distinct personas, Acceleron aids researchers through the formulation of a comprehensive research proposals. It emulates the ideation process, engaging researchers in an interactive fashion to validate the novelty of the proposal and generate plausible set-of hypotheses. Notably, it 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. Our observations and end-user evaluations illustrate the efficacy of Acceleron as an enhancer of researcher{'}s productivity.",
}
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<abstract>In the realm of research support tools, there exists a notable void in resources tailored specifically for aiding researchers during the crucial ideation phase of the research life-cycle. We address this gap by introducing ‘Acceleron’, a ‘Co-Pilot’ for researchers, designed specifically to accelerate the ideation phase of the research life-cycle. Leveraging the reasoning and domain-specific skills of Large Language Models (LLMs) within an agent-based architecture with distinct personas, Acceleron aids researchers through the formulation of a comprehensive research proposals. It emulates the ideation process, engaging researchers in an interactive fashion to validate the novelty of the proposal and generate plausible set-of hypotheses. Notably, it 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. Our observations and end-user evaluations illustrate the efficacy of Acceleron as an enhancer of researcher’s productivity.</abstract>
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%0 Conference Proceedings
%T An Interactive Co-Pilot for Accelerated Research Ideation
%A Nigam, Harshit
%A Patwardhan, Manasi
%A Vig, Lovekesh
%A Shroff, Gautam
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dey, Sunipa
%Y Madaio, Michael
%Y Nenkova, Ani
%Y Yang, Diyi
%Y Xiao, Ziang
%S Proceedings of the Third Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F nigam-etal-2024-interactive
%X In the realm of research support tools, there exists a notable void in resources tailored specifically for aiding researchers during the crucial ideation phase of the research life-cycle. We address this gap by introducing ‘Acceleron’, a ‘Co-Pilot’ for researchers, designed specifically to accelerate the ideation phase of the research life-cycle. Leveraging the reasoning and domain-specific skills of Large Language Models (LLMs) within an agent-based architecture with distinct personas, Acceleron aids researchers through the formulation of a comprehensive research proposals. It emulates the ideation process, engaging researchers in an interactive fashion to validate the novelty of the proposal and generate plausible set-of hypotheses. Notably, it 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. Our observations and end-user evaluations illustrate the efficacy of Acceleron as an enhancer of researcher’s productivity.
%U https://aclanthology.org/2024.hcinlp-1.6
%P 60-73
Markdown (Informal)
[An Interactive Co-Pilot for Accelerated Research Ideation](https://aclanthology.org/2024.hcinlp-1.6) (Nigam et al., HCINLP-WS 2024)
ACL
- Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff. 2024. An Interactive Co-Pilot for Accelerated Research Ideation. In Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 60–73, Mexico City, Mexico. Association for Computational Linguistics.