5 papers
AutoPDL: Automatic Prompt Optimization for LLM Agents
Claudio Spiess, Mandana Vaziri, Louis Mandel +1
The performance of large language models (LLMs) depends on how they are prompted, with choices spanning both the high-level prompting pattern (e.g., Zero-Shot, CoT, ReAct, ReWOO) a…
Representing Prompting Patterns with PDL: Compliance Agent Case Study
Mandana Vaziri, Louis Mandel, Yuji Watanabe +3
Prompt engineering for LLMs remains complex, with existing frameworks either hiding complexity behind restrictive APIs or providing inflexible canned patterns that resist customiza…
Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study
Razan Baltaji, Saurabh Pujar, Louis Mandel +3
Large language models (LLMs) have achieved state-of-the-art performance in various software engineering tasks, including error detection, clone detection, and code translation, pri…
PDL: A Declarative Prompt Programming Language
Mandana Vaziri, Louis Mandel, Claudio Spiess +1
Large language models (LLMs) have taken the world by storm by making many previously difficult uses of AI feasible. LLMs are controlled via highly expressive textual prompts and re…
Insights from the Usage of the Ansible Lightspeed Code Completion Service
Priyam Sahoo, Saurabh Pujar, Ganesh Nalawade +3
The availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environme…