**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models**
**Abstract:**
Scientific research is hindered by its complexity and the need for specialized expertise. To enhance productivity, we propose ResearchAgent, a large language model-powered research idea writing agent. It automatically generates problems, methods, and experiment designs, refining them iteratively based on scientific literature. Starting with a core paper, ResearchAgent is augmented with relevant publications and entities from an entity-centric knowledge store. Multiple ReviewingAgents provide iterative feedback, mirroring human peer discussions. Experiments validate ResearchAgent's effectiveness across multiple disciplines, showing it generates novel, clear, and valid research ideas.
**Introduction:**
Scientific research is crucial for innovation and knowledge advancement. The process involves formulating new ideas and validating them through experiments, typically conducted by humans. However, this is a tedious task requiring extensive knowledge synthesis. Large Language Models (LLMs) have shown impressive capabilities in processing and generating text, making them potential tools to accelerate scientific research. This work focuses on the initial phase of research idea generation, which includes problem identification, method development, and experiment design.
**Related Work:**
Recent works on LLM-augmented scientific discovery focus on experimental validation, while this work targets the more challenging task of generating research ideas. Our approach leverages a knowledge store of entity co-occurrences and iterative refinement with LLM-powered reviewing agents.
**Method:**
ResearchAgent uses LLMs to generate research ideas, incorporating a core paper and its references. It also retrieves relevant entities from a knowledge store to expand contextual knowledge. Iterative refinement is achieved through multiple reviewing agents, whose evaluation criteria are aligned with human preferences.
**Experimental Results:**
ResearchAgent outperforms baselines in generating creative, valid, and clear research ideas. Ablation studies show the importance of both references and entities. Human alignment of evaluation criteria improves the reliability of model-based evaluations. The system's performance is robust to the choice of LLM, with GPT-4 outperforming GPT-3.5.
**Conclusion:**
ResearchAgent accelerates scientific research by generating useful research ideas. Future work could improve the entity-centric knowledge store and explore experimental validation of generated ideas. Ethical considerations are also discussed, emphasizing the need for robustness and safety in LLMs.**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models**
**Abstract:**
Scientific research is hindered by its complexity and the need for specialized expertise. To enhance productivity, we propose ResearchAgent, a large language model-powered research idea writing agent. It automatically generates problems, methods, and experiment designs, refining them iteratively based on scientific literature. Starting with a core paper, ResearchAgent is augmented with relevant publications and entities from an entity-centric knowledge store. Multiple ReviewingAgents provide iterative feedback, mirroring human peer discussions. Experiments validate ResearchAgent's effectiveness across multiple disciplines, showing it generates novel, clear, and valid research ideas.
**Introduction:**
Scientific research is crucial for innovation and knowledge advancement. The process involves formulating new ideas and validating them through experiments, typically conducted by humans. However, this is a tedious task requiring extensive knowledge synthesis. Large Language Models (LLMs) have shown impressive capabilities in processing and generating text, making them potential tools to accelerate scientific research. This work focuses on the initial phase of research idea generation, which includes problem identification, method development, and experiment design.
**Related Work:**
Recent works on LLM-augmented scientific discovery focus on experimental validation, while this work targets the more challenging task of generating research ideas. Our approach leverages a knowledge store of entity co-occurrences and iterative refinement with LLM-powered reviewing agents.
**Method:**
ResearchAgent uses LLMs to generate research ideas, incorporating a core paper and its references. It also retrieves relevant entities from a knowledge store to expand contextual knowledge. Iterative refinement is achieved through multiple reviewing agents, whose evaluation criteria are aligned with human preferences.
**Experimental Results:**
ResearchAgent outperforms baselines in generating creative, valid, and clear research ideas. Ablation studies show the importance of both references and entities. Human alignment of evaluation criteria improves the reliability of model-based evaluations. The system's performance is robust to the choice of LLM, with GPT-4 outperforming GPT-3.5.
**Conclusion:**
ResearchAgent accelerates scientific research by generating useful research ideas. Future work could improve the entity-centric knowledge store and explore experimental validation of generated ideas. Ethical considerations are also discussed, emphasizing the need for robustness and safety in LLMs.