Plan with Code: Comparing approaches for robust NL to DSL generation

Planning in code is considered a more reliable approach for many orchestration tasks. This is because code is more tractable than steps generated via Natural Language and make it easy to support more complex sequences by abstracting deterministic logic into functions. It also allows spotting issues with incorrect function names with the help of parsing checks that can be run on code. Progress in Code Generation methodologies, however, remains limited to general-purpose languages like C, C++, and Python. LLMs continue to face challenges with custom function names in Domain Specific Languages or DSLs, leading to higher hallucination rates and syntax errors. This is more common for custom function names, that are typically part of the plan. Moreover, keeping LLMs up-to-date with newer function names is an issue. This poses a challenge for scenarios like task planning over a large number of APIs, since the plan is represented as a DSL having custom API names. In this paper, we focus on workflow automation in RPA (Robotic Process Automation) domain as a special case of task planning. We present optimizations for using Retrieval Augmented Generation (or RAG) with LLMs for DSL generation along with an ablation study comparing these strategies with a fine-tuned model. Our results showed that the fine-tuned model scored the best on code similarity metric. However, with our optimizations, RAG approach is able to match the quality for in-domain API names in the test set. Additionally, it offers significant advantage for out-of-domain or unseen API names, outperforming Fine-Tuned model on similarity metric by 7 pts.

A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation

Natural Language to Code Generation has made significant progress in recent years with the advent of Large Language Models(LLMs). While generation for general-purpose languages like C, C++, and Python has improved significantly, LLMs struggle with custom function names in Domain Specific Languages or DSLs. This leads to higher hallucination rates and syntax errors, specially for DSLs having a high number of custom function names. Additionally, constant updates to function names add to the challenge as LLMs need to stay up-to-date. In this paper, we present optimizations for using Retrieval Augmented Generation (or RAG) with LLMs for DSL generation along with an ablation study comparing these strategies. We generated a train as well as test dataset with a DSL to represent automation tasks across roughly 700 APIs in public domain. We used the training dataset to fine-tune a Codex model for this DSL. Our results showed that the fine-tuned model scored the best on code similarity metric. With our RAG optimizations, we achieved parity for similarity metric. The compilation rate, however, showed that both the models still got the syntax wrong many times, with RAG-based method being 2 pts better. Conversely, hallucination rate for RAG model lagged by 1 pt for API names and by 2 pts for API parameter keys. We conclude that an optimized RAG model can match the quality of fine-tuned models and offer advantages for new, unseen APIs.