Galactica: A Large Language Model for Science (2211.09085v1)

Abstract: Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. In this paper we introduce Galactica: a LLM that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. It also sets a new state-of-the-art on downstream tasks such as PubMedQA and MedMCQA dev of 77.6% and 52.9%. And despite not being trained on a general corpus, Galactica outperforms BLOOM and OPT-175B on BIG-bench. We believe these results demonstrate the potential for LLMs as a new interface for science. We open source the model for the benefit of the scientific community.

• The paper presents a Transformer-based architecture trained on 48 million curated documents to store and reason about scientific knowledge.
• It introduces a novel working memory token, <work>, that enables step-by-step problem-solving and outperforms GPT-3 and Chinchilla on reasoning benchmarks.
• The model accurately predicts citations and processes multiple modalities such as LaTeX and SMILES, redefining scientific literature organization.

Overview of "Galactica: A LLM for Science"

The paper "Galactica: A LLM for Science" presents a specialized LLM designed to store, combine, and reason about scientific knowledge. Developed by researchers at Meta AI, the model addresses the issue of information overload in the scientific community by organizing vast amounts of scientific data into a coherent and accessible format.

Core Contributions

1. Model Architecture and Training:

Galactica builds upon the Transformer architecture, utilizing a decoder-only setup without biases and employing GeLU activations. The model is trained using a curated dataset of scientific literature comprising 48 million documents, including papers, reference materials, and knowledge bases. The dataset, albeit smaller than those used for general LLMs, is highly curated to enhance quality. Tokenization strategies are adapted for different modalities, such as LaTeX and SMILES, to optimize the representation of scientific data.

2. Reasoning and Knowledge Storage:

Galactica demonstrates advanced capabilities in reasoning tasks, outperforming existing models like GPT-3 and Chinchilla on benchmarks such as MMLU and MATH. A novel feature of the model is the inclusion of a working memory token, <work>, which emulates internal reasoning processes, enabling the model to execute step-by-step problem-solving tasks.

3. Citation Prediction and Literature Organization:

An innovative aspect of Galactica is its ability to predict citations within scientific texts accurately. This capability surpasses traditional retrieval-based approaches, suggesting the model's potential to redefine how scientific literature is organized and accessed.

4. Multi-modal Scientific Tasks:

Galactica is designed to handle multiple scientific modalities. It can parse and generate predictions for chemical properties and annotate protein sequences. The model achieves state-of-the-art results in converting SMILES notations to IUPAC names, emphasizing its versatility in managing diverse scientific data formats.

Performance Evaluation

The paper meticulously evaluates Galactica across various scientific tasks. Notable achievements include:

• Exceeding GPT-3 and other models in reasoning tasks with a performance boost using the <work> token strategy.
• Establishing new benchmarks in tasks requiring deep knowledge, such as equation solving and scientific QA.
• Demonstrating robust performance in citation prediction, validating the model’s ability to learn and replicate the structure of scientific discourse.

Implications and Future Directions

Galactica's development signifies a step toward using LLMs as comprehensive interfaces for scientific knowledge. By outperforming existing models on specific scientific tasks, it shows potential as a tool for research synthesis and exploration, reducing the cognitive load on researchers.

The paper suggests future research could include incorporating retrieval-based enhancements and extending capabilities to handle non-textual data like images, thereby enriching the model’s utility in fields such as drug discovery and protein engineering.

Conclusion

The "Galactica" paper underscores the benefits of a curated, domain-specific approach to LLM development in science. By integrating multi-modal capabilities and advanced reasoning features, Galactica presents a promising framework for advancing how scientific information is accessed and utilized, although further work remains in expanding dataset diversity and model interpretability.