Large Language Models for Automated Open-domain Scientific Hypotheses Discovery (2309.02726v3)

Abstract: Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past research on hypothetical induction is under a constrained setting: (1) the observation annotations in the dataset are carefully manually handpicked sentences (resulting in a close-domain setting); and (2) the ground truth hypotheses are mostly commonsense knowledge, making the task less challenging. In this work, we tackle these problems by proposing the first dataset for social science academic hypotheses discovery, with the final goal to create systems that automatically generate valid, novel, and helpful scientific hypotheses, given only a pile of raw web corpus. 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, which exhibits superior performance in terms of both GPT-4 based and expert-based evaluation. To the best of our knowledge, this is the first work showing that LLMs are able to generate novel (''not existing in literature'') and valid (''reflecting reality'') scientific hypotheses.

• The paper develops the MOOSE framework that integrates multiple modules and feedback loops for iterative refinement of scientific hypotheses.
• It constructs a unique NLP dataset from 50 recent social science publications combined with a supporting web corpus to spur genuine hypothesis generation.
• Experimental results with GPT-3.5 and GPT-4 show that the LLM-driven approach produces more novel and useful hypotheses than traditional baseline methods.

Overview of "LLMs for Automated Open-domain Scientific Hypotheses Discovery"

The paper "LLMs for Automated Open-domain Scientific Hypotheses Discovery" authored by Zonglin Yang et al. introduces a novel initiative to employ LLMs in the automated generation of scientific hypotheses. This research addresses the complex task of hypothetical induction, a form of inductive reasoning crucial for scientific inquiry. The effort is marked by the deployment of a new NLP dataset specifically designed for hypothesis discovery in the field of social sciences, supplemented by a raw web corpus that provides the foundational data necessary for hypothesis generation.

Key Contributions

1. New Dataset Construction: The authors have curated an NLP dataset comprising 50 recent social science publications, complemented by a web corpus sufficient to substantiate hypothesis formulation found in these papers. The dataset distinguishes itself by requiring the derivation of entirely new hypotheses rather than replicating existing knowledge.
2. MOOSE Framework: The proposed framework—Multi-mOdule framewOrk with paSt present future feEdback (MOOSE)—integrates multiple modules into its structure, enabling the iterative refinement of generated hypotheses through the application of different feedback mechanisms. The framework is modular, encompassing stages from background selection to hypothesis proposition and subsequent refinement, illustrating an efficient pipeline for hypothetical induction.
3. Feedback Mechanisms: The innovative use of feedback mechanisms (past-feedback, present-feedback, and future-feedback) ensures the iterative improvement of hypothesis quality. These mechanisms allow for the dynamic assessment and enhancement of the propositions by leveraging the evaluative capabilities of LLMs.

Methodological Insights

The research pivots around extracting viable observations from a vast open corpus and subsequently leveraging this information to originate hypotheses that are both novel and reflective of real-world phenomena. The authors operationalize this through a multi-step process where each stage—background finding, inspiration sourcing, hypothesis suggestion, and evaluation—is underpinned by LLM-driven insights.

The framework's reliance on LLMs, specifically GPT-3.5, examines the models' ability to function as "co-pilots" by generating hypotheses new to current literature, thereby demonstrating the potential of LLMs beyond traditional text generation tasks.

Experimental Findings

Through evaluation via both GPT-4 and social science experts, the researchers establish that MOOSE exhibits superior performance over baseline approaches, notably enhancing the novelty and helpfulness of generated hypotheses. The incorporation of feedback loops demonstrably refines the hypotheses, reflected by progressive improvements across iterative generations.

Implications and Future Directions

This work marks a significant step toward automating scientific discovery processes by deploying LLMs, potentially transforming the way researchers explore new theories and concepts. While currently focused on social sciences, the methodology presents a scalable blueprint applicable to various domains where hypothetical reasoning is foundational.

Future developments could involve expanding the application of MOOSE to different scientific fields, further refinement of feedback mechanisms, and enhancing LLM architectures to maximize hypothesis validity and novelty.

Conclusion

The paper not only contributes a sophisticated methodological framework but also sets a precedent for the growing interface between AI technologies and scientific research processes. As LLMs continue to evolve, their role in hypothesis generation presents a promising frontier for enhancing scientific inquiry and innovation on a global scale.