Big language models (LLMs) are becoming skilled in programming and refactoring code to create libraries for software developers. Researchers from MIT CSAIL, MIT Brain and Cognitive Sciences, and Harvey Mudd College present LILO, a neurosymbolic framework that integrates LLMs with automatic refactoring to learn libraries of reusable function abstractions. LILO demonstrates improved performance compared to conventional techniques and enhances interpretability for easier understanding. The research combines concepts and resources from programming languages and language modeling to advance program synthesis.
Introducing LILO: A Neuro-Symbolic Framework for Learning Interpretable Libraries for Program Synthesis
Big language models (LLMs) are becoming increasingly skilled in programming and solving programming challenges. However, software developers are more interested in creating libraries that can solve whole problem domains. Refactoring, which involves finding abstractions that make the codebase more legible, reusable, and compact, is a crucial component of software development.
In this study, researchers from MIT CSAIL, MIT Brain and Cognitive Sciences, and Harvey Mudd College present LILO, a neurosymbolic framework for Library Induction from Language Observations. LILO integrates language models with algorithmic developments in automatic refactoring to learn libraries of reusable function abstractions.
Key Components of LILO:
- Dual-System Synthesis Module: Uses two different approaches to solve programming problems, combining domain-general priors and domain-specific expressions.
- Compression Module: Utilizes STITCH, a symbolic compression system, to find relevant abstractions from the current solution set.
- Auto-Documentation Module: Generates docstrings and function names that are legible by humans, enhancing interpretability and facilitating future searches.
LILO’s design is based on the iterative Wake-Sleep algorithm DREAMCODER, which alternates between finding solutions to programming challenges and rewriting common abstractions into a library. LILO outperforms conventional deep learning techniques by drawing significant generalizations from a small number of samples.
Compared to DreamCoder, LILO completes more programming tasks and learns empirically richer libraries. LILO’s AutoDoc module enhances interpretability and facilitates better utilization of the library by the language model synthesizer.
If you want to evolve your company with AI and stay competitive, consider using LILO to learn interpretable libraries for program synthesis. Discover how AI can redefine your way of work and identify automation opportunities. Define KPIs to ensure measurable impacts on business outcomes and select an AI solution that aligns with your needs. Implement AI gradually, starting with a pilot and expanding usage judiciously.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- MIT Researchers Introduce LILO: A Neuro-Symbolic Framework for Learning Interpretable Libraries for Program Synthesis
- MarkTechPost
- Twitter – @itinaicom
