Chenglei Si, Diyi Yang, Tatsunori Hashimoto
This paper, “Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers,” provides compelling evidence that large language models (LLMs) can significantly contribute to the generation of novel research ideas, often surpassing human experts in terms of novelty. Through a rigorous experimental design involving over 100 NLP researchers and detailed statistical analysis, the study found that LLM-generated ideas were judged to be more novel but slightly less feasible compared to human-generated ideas. This finding points to the creative potential of LLMs while also highlighting their limitations in practical implementation. The paper introduces a robust evaluation framework that incorporates multi-faceted metrics like novelty, excitement, and feasibility, setting a high standard for future assessments of AI capabilities in ideation. By leveraging Retrieval-Augmented Generation (RAG), the study enhances the factual accuracy and relevance of the generated ideas, showcasing the effective integration of existing knowledge. This research is particularly valuable as it not only underscores the strengths of LLMs in creative tasks but also identifies areas for improvement, such as diversity in idea generation and reliable self-evaluation. The proposed future work, including the execution and long-term assessment of both AI-generated and human-generated ideas, presents a promising direction for further research. This paper is essential reading for those interested in AI-assisted innovation, as it offers insights into harnessing AI to complement human creativity and uncover potentially overlooked ideas. The example code illustrates the concept with a diversified list of research ideas and uses semantic scholar API for its paper reading step.
Mind Map
High resolution PDF and interactive HTML can be found at the Github repo.
Highlights explained
1. LLMs Generate More Novel Research Ideas
Explanation:
Large Language Models (LLMs) are capable of generating research ideas that are evaluated as significantly more novel than those created by human experts. This was demonstrated across multiple statistical tests and conditions in the study.
Significance:
Novelty is a key indicator of creativity and innovation in research. The ability of LLMs to consistently produce novel ideas suggests their potential to contribute significantly to fields requiring creative problem-solving and ideation.
Relation to Existing Work:
This finding contrasts with earlier studies that primarily focused on the feasibility of LLM-generated outputs in specific tasks like code generation or mathematical problem-solving. Here, it showcases the broader creative potential of LLMs across scientific research ideation.
2. Challenges in Feasibility of LLM-Generated Ideas
Explanation:
While LLM-generated ideas are more novel, they tend to be judged as less feasible compared to human-generated ideas. Feasibility issues stem from LLMs’ tendency to overlook practical implementation details and resource constraints.
Significance:
Feasibility is critical for the practical execution of research projects. Identifying this gap highlights areas where LLMs need improvement, particularly in grounding their creative outputs within realistic constraints.
Relation to Existing Work:
Previous work has shown LLMs to be excellent at generating creative solutions but often lacking in producing practically implementable results. This study reaffirms that, although novel, LLM ideas require further refinement to be actionable.
3. Improved Evaluation Methods for AI-Generated Ideas
Explanation:
The paper proposes an innovative evaluation framework that includes human expert reviews and statistical rigor to assess the quality of AI-generated research ideas. This includes a variety of metrics like novelty, excitement, feasibility, and overall effectiveness.
Significance:
A robust evaluation framework is essential for objectively measuring the potential of AI-generated research ideas. By incorporating multi-faceted metrics and thorough statistical analysis, the study sets a high standard for future evaluations.
Relation to Existing Work:
Unlike other studies that rely on limited metrics or smaller scales of evaluation, this comprehensive framework provides a more holistic and rigorous assessment of LLM capabilities in ideation.
4. Role of Retrieval-Augmented Generation (RAG)
Explanation:
The study employs Retrieval-Augmented Generation (RAG) to enhance LLM performance by grounding idea generation in existing literature and knowledge. This involves retrieving relevant papers and using them to inform and refine generated ideas.
Significance:
RAG helps in reducing hallucinations and improving the factual accuracy of LLM outputs. It leverages vast amounts of existing knowledge to produce more informed and contextually relevant ideas.
Relation to Existing Work:
RAG is a method increasingly used to improve the performance of LLMs in knowledge-intensive tasks. This study effectively applies RAG to the domain of research ideation, demonstrating its broader applicability.
Code
The PoC implementation uses Semantic Scholar API to simulate the research due diligence process, which search for related papers for given initial thought. The API may enforce rate limit for queries without API key. Please apply for one and use it as S2_API_KEY if needed.
pip install openai numpy scikit-learn requestsimport os
import json
import requests
from time import sleep
from openai import OpenAI
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
# Initialize OpenAI client
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
def search_semantic_scholar(query, limit=10):
"""
Retrieves relevant papers from Semantic Scholar API.
This function implements the Retrieval-Augmented Generation (RAG) aspect of the paper.
"""
base_url = "https://api.semanticscholar.org/graph/v1"
search_url = f"{base_url}/paper/search"
api_key = os.environ.get("S2_API_KEY")
headers = {"x-api-key": api_key} if api_key else {}
params = {
"query": query,
"fields": "title,abstract,year,citationCount,authors,venue",
"limit": limit
}
response = requests.get(search_url, params=params, headers=headers)
if response.status_code != 200:
print(f"Error: API request failed with status code {response.status_code}")
return None
return response.json().get('data', [])
def generate_seed_ideas(topic, context, num_seeds=20):
"""
Generates initial seed ideas.
This function implements the Seed Idea Generation step from the paper.
It uses chain-of-thought prompting to encourage more detailed reasoning.
"""
print(f"Generating {num_seeds} seed ideas for topic: {topic}")
prompt = f"""Given the following context about the topic "{topic}":
{context}
Generate a brief seed for a novel research idea. The idea should be:
1. Novel and different from existing work
2. Feasible to implement
3. Potentially impactful in the field
Use the following steps:
1. Identify key challenges or gaps in the current research
2. Brainstorm potential solutions or novel approaches
3. Consider interdisciplinary connections
4. Formulate a concise research idea
Provide the seed idea in the following JSON format:
{{
"title": "Brief title of the idea",
"description": "One-sentence description of the idea"
}}
"""
seeds = []
for i in range(num_seeds):
print(f"Generating seed idea {i+1}/{num_seeds}")
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are an expert AI research assistant capable of generating novel research ideas."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
seed = json.loads(response.choices[0].message.content)
seeds.append(seed)
print(f"Generated seed idea:")
print(f"Title: {seed['title']}")
print(f"Description: {seed['description']}")
print()
return seeds
def deduplicate_ideas(ideas, similarity_threshold=0.8):
"""
Removes duplicate or very similar ideas.
This function implements the deduplication step mentioned in the paper,
using embedding similarity to identify and remove near-duplicate ideas.
"""
print(f"Deduplicating {len(ideas)} ideas...")
def get_embedding(text):
response = client.embeddings.create(input=text, model="text-embedding-ada-002")
return response.data[0].embedding
embeddings = [get_embedding(idea) for idea in ideas]
similarity_matrix = cosine_similarity(embeddings)
unique_ideas = []
for i, idea in enumerate(ideas):
if not any(similarity_matrix[i][j] > similarity_threshold for j in range(i) if i != j):
unique_ideas.append(idea)
print(f"Reduced to {len(unique_ideas)} unique ideas")
return unique_ideas
def expand_idea(seed_idea, topic, context):
"""
Expands a seed idea into a full research proposal.
This function implements the Idea Expansion step from the paper,
using chain-of-thought prompting to guide the expansion process.
"""
print(f"Expanding seed idea: {seed_idea['title']}")
prompt = f"""Expand the following seed idea into a full research proposal related to the topic "{topic}":
Seed Idea: {seed_idea['title']} - {seed_idea['description']}
Context:
{context}
Use the following steps to expand the idea:
1. Clearly define the problem and its significance
2. Analyze existing methods and their limitations
3. Explain the motivation behind your proposed approach
4. Describe your proposed method in detail, including potential challenges and how to address them
Provide the expanded idea in the following JSON format:
{{
"title": "Title of the research idea",
"problem_statement": "Brief statement of the problem being addressed",
"existing_methods": "Overview of current approaches",
"motivation": "Why this idea is important and novel",
"proposed_method": "Detailed description of the proposed approach"
}}
"""
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are an expert AI research assistant capable of expanding research ideas into full proposals."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
return json.loads(response.choices[0].message.content)
def rank_idea(idea):
"""
Ranks an expanded idea based on novelty, feasibility, and potential impact.
This function implements the Idea Ranking step from the paper,
using chain-of-thought prompting to encourage detailed evaluation.
"""
print(f"Ranking idea: {idea['title']}")
prompt = f"""Evaluate the following research idea on a scale of 1-10 for each of novelty, feasibility, and potential impact.
Provide a brief justification for each score.
{json.dumps(idea, indent=2)}
Use the following steps for evaluation:
1. Assess novelty: Consider how different the idea is from existing work
2. Evaluate feasibility: Consider technical challenges, required resources, and timeline
3. Gauge potential impact: Consider both academic and practical implications
4. Provide an overall score as an average of the three dimensions
Respond in the following JSON format:
{{
"novelty": {{
"score": float,
"justification": "string"
}},
"feasibility": {{
"score": float,
"justification": "string"
}},
"potential_impact": {{
"score": float,
"justification": "string"
}},
"overall_score": float
}}
"""
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are an expert AI research evaluator capable of assessing the quality of research ideas."},
{"role": "user", "content": prompt}
],
response_format={"type": "json_object"}
)
evaluation = json.loads(response.choices[0].message.content)
return evaluation['overall_score'], json.dumps(evaluation, indent=2)
def human_rerank(ranked_ideas):
"""
Simulates human reranking of the top ideas.
In a real scenario, this function would involve actual human input.
"""
print("Top 5 AI-ranked ideas:")
for i, (idea, score, evaluation) in enumerate(ranked_ideas[:5], 1):
print(f"{i}. Score: {score:.2f}")
print(f"Title: {idea['title']}")
print(f"Problem Statement: {idea['problem_statement'][:200]}...")
print()
print("Simulating human reranking...")
# In a real scenario, we would implement a way for human input here
# For simulation, we'll just shuffle the top 5
top_5 = ranked_ideas[:5]
np.random.shuffle(top_5)
return top_5 + ranked_ideas[5:]
def generate_research_ideas(topic):
"""
Main function that orchestrates the entire idea generation process.
This function implements the full pipeline described in the paper:
1. Retrieval of relevant papers
2. Generation of seed ideas
3. Deduplication of ideas
4. Expansion of top ideas
5. Ranking of expanded ideas
6. Human reranking (simulated)
"""
print(f"\n--- Generating research ideas for topic: {topic} ---")
# Get relevant papers from Semantic Scholar
papers = search_semantic_scholar(topic)
if not papers:
print(f"No papers found for topic: {topic}")
return None
print(f"Retrieved {len(papers)} relevant papers from Semantic Scholar")
# Prepare context from the papers
context = "\n".join([f"Title: {p['title']}\nAbstract: {p.get('abstract', 'N/A')}\nYear: {p.get('year', 'N/A')}\nCitations: {p.get('citationCount', 'N/A')}\n" for p in papers])
# Generate seed ideas
seed_ideas = generate_seed_ideas(topic, context, num_seeds=20) # Reduced for demonstration, paper uses 4000
# Deduplicate seed ideas
unique_seeds = deduplicate_ideas([json.dumps(seed) for seed in seed_ideas])
unique_seeds = [json.loads(seed) for seed in unique_seeds]
# Expand ideas
print("Expanding top 5 unique ideas...")
expanded_ideas = [expand_idea(seed, topic, context) for seed in unique_seeds[:5]] # Expand top 5 for demonstration
# Rank ideas
print("Ranking expanded ideas...")
ranked_ideas = [(idea, *rank_idea(idea)) for idea in expanded_ideas]
ranked_ideas.sort(key=lambda x: x[1], reverse=True)
# Human reranking
final_ranking = human_rerank(ranked_ideas)
return final_ranking
def main():
topics = [
"Novel prompting methods to reduce hallucinations in large language models",
"Improving factual consistency in language model outputs",
"Enhancing cross-lingual transfer in low-resource languages",
"Developing more efficient fine-tuning techniques for large language models",
"Improving mathematical reasoning capabilities of language models",
"Quantum computing applications in cryptography",
"CRISPR gene editing for treating genetic disorders",
"Nanomaterials for efficient solar energy conversion",
"Machine learning in drug discovery and development",
"Neuroplasticity and cognitive enhancement techniques",
"Dark matter detection methods in astrophysics",
"Microbiome manipulation for improving crop yields",
"Sustainable alternatives to lithium-ion batteries",
"Artificial photosynthesis for carbon capture",
"Bioengineered organs for transplantation"
]
all_generated_ideas = {}
for topic in topics:
ideas = generate_research_ideas(topic)
if ideas:
all_generated_ideas[topic] = ideas
print(f"Generated {len(ideas)} ideas for topic: {topic}")
else:
print(f"Failed to generate ideas for topic: {topic}")
sleep(1) # To avoid hitting API rate limits
# Save generated ideas to a file
with open('generated_research_ideas.json', 'w') as f:
json.dump(all_generated_ideas, f, indent=2)
print("\nAll generated ideas have been saved to 'generated_research_ideas.json'")
if __name__ == "__main__":
main()