What does this research mean for the field?

Large multimodal language models are transforming the entire scientific lifecycle by enabling AI-assisted literature search, experimentation, content generation, and peer review, while also introducing new ethical and research integrity risks. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To provide a comprehensive overview of core techniques, datasets, and evaluation methodologies for deploying large multimodal language models across the scientific lifecycle.

September 7, 2026ACM Computing SurveysOpen Access

Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation

View Full Paper

Ask AI

Bookmark

Authors

SESteffen Eger CYCao Yong JDJennifer D’Souza

Discussion

Member takes

Overview

Literature survey demonstrates the transformative impact of large language models across scientific research, highlighting emerging discovery tools and critical risks to research integrity.

Key Points

To provide a comprehensive overview of core techniques, datasets, and evaluation methodologies for deploying large multimodal language models across the scientific lifecycle.
Curated literature across five scientific phases: literature search, hypothesis generation and experimentation, text production, multimodal artifact generation, and peer review.
Assessed evaluation benchmarks, methodological trends, systemic limitations, and ethical risks across generative scientific tools.
Cataloged rapid progress in multimodal language models capable of formulating novel research questions, drafting manuscripts, and synthesizing figures.
Identified significant challenges regarding hallucinated outputs, reproducibility limitations, and emerging risks to peer-review integrity.

Cite This Study

Eger et al. (2026) studied this question.

synapsesocial.com/papers/6a9e8531c3034f961570d552 https://doi.org/10.1145/3845596

View Full Paper

Ask AI

Bookmark

September 7, 2026ACM Computing SurveysOpen Access

Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation

View Full Paper

Ask AI

Bookmark

Authors

SESteffen Eger CYCao Yong JDJennifer D’Souza

Discussion

Member takes

Overview

Literature survey demonstrates the transformative impact of large language models across scientific research, highlighting emerging discovery tools and critical risks to research integrity.

Key Points

To provide a comprehensive overview of core techniques, datasets, and evaluation methodologies for deploying large multimodal language models across the scientific lifecycle.
Curated literature across five scientific phases: literature search, hypothesis generation and experimentation, text production, multimodal artifact generation, and peer review.
Assessed evaluation benchmarks, methodological trends, systemic limitations, and ethical risks across generative scientific tools.
Cataloged rapid progress in multimodal language models capable of formulating novel research questions, drafting manuscripts, and synthesizing figures.
Identified significant challenges regarding hallucinated outputs, reproducibility limitations, and emerging risks to peer-review integrity.

Cite This Study

Eger et al. (2026) studied this question.

synapsesocial.com/papers/6a9e8531c3034f961570d552 https://doi.org/10.1145/3845596

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark