OpenScholar: Your AI research assistant that actually reads and cites papers correctly
OpenScholar is a specialized retrieval-augmented LLM that synthesizes scientific literature by identifying relevant passages from 45 million open-access papers and generating citation-backed responses. It outperforms GPT-4 in correctness and citation accuracy, while being smaller and fully open-source.
-----
https://arxiv.org/abs/2411.14199
🔍 Original Problem:
Scientists struggle to stay informed due to the massive volume of published papers. Existing LLMs often hallucinate citations and rely on outdated data, making them unreliable for scientific literature synthesis.
-----
🛠️ Solution in this Paper:
→ OpenScholar combines a specialized datastore of 45 million papers with trained retrievers and an 8B parameter LM
→ It uses a self-feedback inference loop that iteratively improves responses through retrieval and refinement
→ The system generates synthetic training data by having larger models create high-quality examples
→ Citations are verified through a dedicated post-processing step to ensure accuracy
-----
💡 Key Insights:
→ Retrieval-augmented architectures can effectively combat hallucination in scientific tasks
→ Self-feedback loops significantly improve response quality and citation accuracy
→ Synthetic data generation enables training smaller, efficient models that maintain performance
-----
📊 Results:
→ OpenScholar-8B outperforms GPT-4 by 5% in correctness
→ Achieves citation accuracy comparable to human experts
→ Experts preferred OpenScholar responses over human-written ones 51% of the time
→ When combined with GPT-4, improves correctness by 12%
Share this post