Large Language Models are Zero Shot Hypothesis Proposers

• URL: http://arxiv.org/abs/2311.05965v1
• Date: Fri, 10 Nov 2023 10:03:49 GMT
• Title: Large Language Models are Zero Shot Hypothesis Proposers
• Authors: Biqing Qi, Kaiyan Zhang, Haoxiang Li, Kai Tian, Sihang Zeng, Zhang-Ren Chen, Bowen Zhou
• Abstract summary: Large Language Models (LLMs) hold a wealth of global and interdisciplinary knowledge that promises to break down information barriers. We construct a dataset consist of background knowledge and hypothesis pairs from biomedical literature. We evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings.
• Score: 17.612235393984744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Significant scientific discoveries have driven the progress of human civilisation. The explosion of scientific literature and data has created information barriers across disciplines that have slowed the pace of scientific discovery. Large Language Models (LLMs) hold a wealth of global and interdisciplinary knowledge that promises to break down these information barriers and foster a new wave of scientific discovery. However, the potential of LLMs for scientific discovery has not been formally explored. In this paper, we start from investigating whether LLMs can propose scientific hypotheses. To this end, we construct a dataset consist of background knowledge and hypothesis pairs from biomedical literature. The dataset is divided into training, seen, and unseen test sets based on the publication date to control visibility. We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs. Additionally, we introduce an LLM-based multi-agent cooperative framework with different role designs and external tools to enhance the capabilities related to generating hypotheses. We also design four metrics through a comprehensive review to evaluate the generated hypotheses for both ChatGPT-based and human evaluations. Through experiments and analyses, we arrive at the following findings: 1) LLMs surprisingly generate untrained yet validated hypotheses from testing literature. 2) Increasing uncertainty facilitates candidate generation, potentially enhancing zero-shot hypothesis generation capabilities. These findings strongly support the potential of LLMs as catalysts for new scientific discoveries and guide further exploration.

Related papers

• Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models [20.648157071328807]
  Large language models (LLMs) can identify novel research directions by analyzing existing knowledge. LLMs are prone to generating hallucinations'', outputs that are plausible-sounding but factually incorrect. We propose KG-CoI, a system that enhances LLM hypothesis generation by integrating external, structured knowledge from knowledge graphs.
  arXiv  Detail & Related papers  (2024-11-04T18:50:00Z)
• Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation [15.495976478018264]
  Large language models (LLMs) have emerged as a promising tool to revolutionize knowledge interaction. We construct a dataset of background-hypothesis pairs from biomedical literature, partitioned into training, seen, and unseen test sets. We assess the hypothesis generation capabilities of top-tier instructed models in zero-shot, few-shot, and fine-tuning settings.
  arXiv  Detail & Related papers  (2024-07-12T02:55:13Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
  We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
  arXiv  Detail & Related papers  (2024-04-01T17:45:15Z)
• Large language models surpass human experts in predicting neuroscience results [60.26891446026707]
  Large language models (LLMs) forecast novel results better than human experts. BrainBench is a benchmark for predicting neuroscience results. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.
  arXiv  Detail & Related papers  (2024-03-04T15:27:59Z)
• Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
  Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
  arXiv  Detail & Related papers  (2024-01-26T05:33:34Z)
• Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences [3.9985385067438344]
  A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. With exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences.
  arXiv  Detail & Related papers  (2023-09-07T04:15:17Z)
• Large Language Models for Automated Open-domain Scientific Hypotheses Discovery [50.40483334131271]
  This work proposes the first dataset for social science academic hypotheses discovery. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi- module framework is developed for the task, including three different feedback mechanisms to boost performance.
  arXiv  Detail & Related papers  (2023-09-06T05:19:41Z)

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.