An Explanatory Query-Based Framework for Exploring Academic Expertise
- URL: http://arxiv.org/abs/2105.13728v2
- Date: Mon, 31 May 2021 10:46:56 GMT
- Title: An Explanatory Query-Based Framework for Exploring Academic Expertise
- Authors: Oana Cocarascu, Andrew McLean, Paul French, Francesca Toni
- Abstract summary: Finding potential collaborators in institutions is a time-consuming manual search task prone to bias.
We propose a novel query-based framework for searching, scoring, and exploring research expertise automatically.
We show that our simple method is effective in identifying matches, while satisfying desirable properties and being efficient.
- Score: 10.887008988767061
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The success of research institutions heavily relies upon identifying the
right researchers "for the job": researchers may need to identify appropriate
collaborators, often from across disciplines; students may need to identify
suitable supervisors for projects of their interest; administrators may need to
match funding opportunities with relevant researchers, and so on. Usually,
finding potential collaborators in institutions is a time-consuming manual
search task prone to bias. In this paper, we propose a novel query-based
framework for searching, scoring, and exploring research expertise
automatically, based upon processing abstracts of academic publications. Given
user queries in natural language, our framework finds researchers with relevant
expertise, making use of domain-specific knowledge bases and word embeddings.
It also generates explanations for its recommendations. We evaluate our
framework with an institutional repository of papers from a leading university,
using, as baselines, artificial neural networks and transformer-based models
for a multilabel classification task to identify authors of publication
abstracts. We also assess the cross-domain effectiveness of our framework with
a (separate) research funding repository for the same institution. We show that
our simple method is effective in identifying matches, while satisfying
desirable properties and being efficient.
Related papers
- Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents [64.64280477958283]
An exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions.
Recent developments in large language models(LLMs) suggest a promising avenue for automating the generation of novel research ideas.
We propose a Chain-of-Ideas(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain.
arXiv Detail & Related papers (2024-10-17T03:26:37Z) - Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature [48.572336666741194]
We present Knowledge Navigator, a system designed to enhance exploratory search abilities.
It organizes retrieved documents into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics.
arXiv Detail & Related papers (2024-08-28T14:48:37Z) - ResearchArena: Benchmarking LLMs' Ability to Collect and Organize Information as Research Agents [21.17856299966841]
Large language models (LLMs) have exhibited remarkable performance across various tasks in natural language processing.
We develop ResearchArena, a benchmark that measures LLM agents' ability to conduct academic surveys.
arXiv Detail & Related papers (2024-06-13T03:26:30Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is a large language model-powered research idea writing agent.
It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers.
SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level.
Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
arXiv Detail & Related papers (2024-04-09T15:01:51Z) - Large Search Model: Redefining Search Stack in the Era of LLMs [63.503320030117145]
We introduce a novel conceptual framework called large search model, which redefines the conventional search stack by unifying search tasks with one large language model (LLM)
All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts.
This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack.
arXiv Detail & Related papers (2023-10-23T05:52:09Z) - Promoting Research Collaboration with Open Data Driven Team
Recommendation in Response to Call for Proposals [10.732914229005903]
We describe a novel system to recommend teams using a variety of AI methods.
We create teams to maximize goodness along a metric balancing short- and long-term objectives.
arXiv Detail & Related papers (2023-09-18T00:04:08Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Artificial Intelligence for Scientific Research: Authentic Research Education Framework [6.772344064510275]
We implement a program in which teams of students with complementary skills develop useful artificial intelligence (AI) solutions for researchers in natural sciences.
Our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs.
arXiv Detail & Related papers (2022-09-19T16:50:05Z) - Effective Distributed Representations for Academic Expert Search [1.9815631757151737]
We study how different distributed representations of academic papers (i.e. embeddings) impact academic expert retrieval.
In particular, we explore the impact of the use of contextualized embeddings on search performance.
We observe that using contextual embeddings produced by a transformer model trained for sentence similarity tasks produces the most effective paper representations.
arXiv Detail & Related papers (2020-10-16T09:43:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.