A systematic framework for generating novel experimental hypotheses from language models

[2408.05086] A systematic framework for generating novel experimental hypotheses from language models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2408.05086 (cs) [Submitted on 9 Aug 2024 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: A systematic framework for generating novel experimental hypotheses from language models Authors: Kanishka Misra , Najoung Kim View a PDF of the paper titled A systematic framework for generating novel experimental hypotheses from language models, by Kanishka Misra and 1 other authors View PDF HTML (experimental) Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broadly, human cognition, remains debated. While existing work in this area often evaluates human-machine alignment, few studies attempt to translate findings from this enterprise into novel insights about humans. To this end, we propose a systematic framework for hypothesis generation that uses LMs to simulate outcomes of experiments that do not yet exist in the literature. We instantiate this framework in the context of a specific research question in child language development: dative verb acquisition and cross-structural generalization. Through this instantiation, we derive novel, untested hypotheses: the alignment between argument ordering and discourse prominence features of exposure contexts modulates how children generalize new verbs to unobserved structures. Additionally, we also design a set of experiments that can test these hypotheses in the lab with children. This work contributes both a domain-general framework for systematic hypothesis generation via simulated learners and domain-specific, lab-testable hypotheses for child language acquisition research. Comments: Revised version Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2408.05086 [cs.CL] (or arXiv:2408.05086v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2408.05086 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kanishka Misra [ view email ] [v1] Fri, 9 Aug 2024 14:17:36 UTC (902 KB) [v2] Mon, 28 Oct 2024 14:30:13 UTC (1,860 KB) [v3] Thu, 9 Apr 2026 04:08:22 UTC (636 KB) Full-text links: Access Paper: View a PDF of the paper titled A systematic framework for generating novel experimental hypotheses from language models, by Kanishka Misra and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact#290636
• Exploring the role of large language models in the scientific method: from hypothesis to discovery#627385
• Evaluating the utility of large language models for detecting and simulating language dysfunction.#354479
• Beyond Accuracy: Cross-Linguistic Equity and Socio-Technical Dimensions of Large Language Models#19968
• Assessing the Validity of Large Language Models as Synthetic Survey Respondents: Autonomous Completion, Internal Consistency, and Demographic Fidelity#414642