# Scientific Knowledge Discovery in the Age of Large Language Models

arXiv: https://arxiv.org/abs/2607.26670 · HTML: https://mlsanity.com/paper/2607.26670/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers](https://mlsanity.com/paper/2507.02694/) (2025)
- [The strain on scientific publishing](https://mlsanity.com/paper/2309.15884/) (2023)
- [Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking](https://mlsanity.com/paper/2505.21815/) (2025)
- [An automatic patent literature retrieval system based on LLM-RAG](https://mlsanity.com/paper/2508.14064/) (2025)
- [TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering](https://mlsanity.com/paper/2411.06024/) (2024)
- [Streamlining Systematic Reviews: A Novel Application of Large Language Models](https://mlsanity.com/paper/2412.15247/) (2024)
- [BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs](https://mlsanity.com/paper/2510.13926/) (2025)
- [Academic information retrieval using citation clusters: In-depth evaluation based on systematic reviews](https://mlsanity.com/paper/2207.03299/) (2022)
- [BIP! DB: A Dataset of Impact Measures for Scientific Publications](https://mlsanity.com/paper/2101.12001/) (2021)
- [Conversational Exploration of Literature Landscape with LitChat](https://mlsanity.com/paper/2505.23789/) (2025)
- [Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning](https://mlsanity.com/paper/2508.05129/) (2025)

## Abstract (arXiv)

The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.