Abstract-Level Summary

The research explores the adaptation of large language models (LLMs) as autonomous agents in various environments through a novel data-centric framework called Learn-by-interact. This approach synthesizes high-quality agent data without human input by creating interactions between LLMs and environments and then distilling these into instructional data using backward construction. Evaluations across digital platforms, such as SWE-bench and WebArena, show significant performance improvements, with up to a 12.2% increase in instruction-in-context learning (ICL) and up to 19.5% improvement in training scenarios. The framework provides a cost-effective alternative for adapting LLMs robustly to realistic tasks.

Introduction Highlights

The issue addressed is the performance gap of LLMs in real-world applications due to inadequate adaptation to diverse environments. The study aims to bridge this gap using Learn-by-interact, which autonomously synthesizes data for LLM adaptation without the need for costly human annotations. The hypothesis is that interactions generate instructive feedback for better model adaptation.

Methodology

The study employs a synthesis and filtering process wherein LLMs interact with environments, generating trajectories and reconstructing task instructions via backward construction. The synthesized data is filtered for quality and used in both ICL and model training. Benchmark assessments use datasets like SWE-bench and WebArena, with a focus on improving performance over baseline models.

Key Findings

• Implementing Learn-by-interact in ICL improved baseline performances by up to 12.2%.
• Training with synthesized data achieved enhancements of up to 19.5%, notably on WebArena.
• Backward construction and novel retrieval pipelines contributed to these gains, enhancing both the quantity and quality of instructional data.

Implications and Contributions

The research underscores the potential of Learn-by-interact in improving LLM adaptability to varied environments efficiently. The framework could significantly aid in fields where high-quality adaptation of AI models is crucial, such as software development and web navigation. By eliminating human annotation costs, it paves the way for scalable LLM applications.

Conclusion

Learn-by-interact significantly enhances model accuracy and adaptability under realistic conditions while reducing latency and computational load. Its limitation lies in the extensive computational resources needed for data generation and filtering. Future efforts should focus on multi-modal settings and improving annotation efficiency.

Glossary

1. Large Language Models (LLMs): Advanced AI models that process and generate human language text based on a vast corpus of text data.
2. In-Context Learning (ICL): A training method where a model learns to perform tasks by seeing examples and tasks within the same input prompt.
3. Backward Construction: A method of generating new task instructions by summarizing or abstracting from interaction histories.
4. Trajectory: The sequence of actions and observations an agent experiences during interactions with an environment.
5. Retrieval-Augmented Generation (RAG): An approach where retrieval mechanisms enrich LLM inputs with relevant information to improve outputs.
6. Agentic Retrieval: Specialized retrieval methods tailored for agent tasks, enhancing data usage for AI models.
7. Self-Instruct: A process enabling LLMs to autonomously generate task instructions based on standard resources like documentation.