# Can Large Language Models Unlock Novel Scientific Research Ideas?

arXiv: https://arxiv.org/abs/2409.06185 · HTML: https://mlsanity.com/paper/2409.06185/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019)
- [Do text-free diffusion models learn discriminative visual representations?](https://mlsanity.com/paper/2311.17921/) (2023)
- [Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study](https://mlsanity.com/paper/2106.09700/) (2021)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [Exploring and Verbalizing Academic Ideas by Concept Co-occurrence](https://mlsanity.com/paper/2306.02282/) (2023)
- [PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning](https://mlsanity.com/paper/2305.19472/) (2023)
- [Fine-Tuning Generative Models as an Inference Method for Robotic Tasks](https://mlsanity.com/paper/2310.12862/) (2023)
- [Generative Models for 3D Point Clouds](https://mlsanity.com/paper/2302.13408/) (2023)
- [When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews](https://mlsanity.com/paper/2310.18685/) (2023)

## Built on by (most cited first, 24 of 24)

- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses](https://mlsanity.com/paper/2410.07076/) (2024)
- [ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition](https://mlsanity.com/paper/2503.21248/) (2025)
- [SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers](https://mlsanity.com/paper/2504.00255/) (2025)
- [AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay](https://mlsanity.com/paper/2502.16789/) (2025)
- [Sparks of Science: Hypothesis Generation Using Structured Paper Data](https://mlsanity.com/paper/2504.12976/) (2025)
- [AAAR-1.0: Assessing AI's Potential to Assist Research](https://mlsanity.com/paper/2410.22394/) (2024)
- [ResearchGym: Evaluating Language Model Agents on Real-World AI Research](https://mlsanity.com/paper/2602.15112/) (2026)
- [IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2504.16728/) (2025)
- [AIGS: Generating Science from AI-Powered Automated Falsification](https://mlsanity.com/paper/2411.11910/) (2024)
- [LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research](https://mlsanity.com/paper/2412.14141/) (2024)
- [MIR: Methodology Inspiration Retrieval for Scientific Research Problems](https://mlsanity.com/paper/2506.00249/) (2025)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Learning to Predict Future-Aligned Research Proposals with Language Models](https://mlsanity.com/paper/2603.27146/) (2026)
- [NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment](https://mlsanity.com/paper/2604.11543/) (2026)
- [GoAI: Enhancing AI Students' Learning Paths and Idea Generation via Graph of AI Ideas](https://mlsanity.com/paper/2503.08549/) (2025)
- [Idea2Plan: Exploring AI-Powered Research Planning](https://mlsanity.com/paper/2510.24891/) (2025)
- [MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier](https://mlsanity.com/paper/2603.03756/) (2026)
- [Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts](https://mlsanity.com/paper/2606.09105/) (2026)
- [EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation](https://mlsanity.com/paper/2605.30961/) (2026)
- [Ideation Arena: Evaluating LLM Generated Research Ideas with Battle-style Human Expert Assessment](https://mlsanity.com/paper/2608.29696/) (2026)
- [What Are Research Hypotheses?](https://mlsanity.com/paper/2509.00185/) (2025)
- [MAIL: Memory-driven, Adaptive, Incremental, and Literature-grounded Framework for Hypothesis Generation in Chemistry](https://mlsanity.com/paper/2608.28315/) (2026)
- [SciLENS: RL-Driven Autonomous Agents for Scientific Localized Evidence Navigation and Synthesis](https://mlsanity.com/paper/2609.03338/) (2026)

## Abstract (arXiv)

The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people's everyday lives. This study examines the ability of Large Language Models (LLMs) to generate future research ideas from scientific papers. Unlike tasks such as summarization or translation, idea generation lacks a clearly defined reference set or structure, making manual evaluation the default standard. However, human evaluation in this setting is extremely challenging ie: it requires substantial domain expertise, contextual understanding of the paper, and awareness of the current research landscape. This makes it time-consuming, costly, and fundamentally non-scalable, particularly as new LLMs are being released at a rapid pace. Currently, there is no automated evaluation metric specifically designed for this task. To address this gap, we propose two automated evaluation metrics: Idea Alignment Score (IAScore) and Idea Distinctness Index. We further conducted human evaluation to assess the novelty, relevance, and feasibility of the generated future research ideas. This investigation offers insights into the evolving role of LLMs in idea generation, highlighting both its capability and limitations. Our work contributes to the ongoing efforts in evaluating and utilizing language models for generating future research ideas. We make our datasets and codes publicly available