Summary of Appl: a Prompt Programming Language For Harmonious Integration Of Programs and Large Language Model Prompts, by Honghua Dong et al.
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
by Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel language model prompt programming language, APPL, is proposed to simplify the integration of Large Language Models (LLMs) into complex workflows. APPL seamlessly embeds prompts into Python functions and vice versa, providing an intuitive syntax, efficient parallelized runtime, and tracing module for effective failure diagnosis. The language is demonstrated through three scenarios: Chain-of-Thought with self-consistency, ReAct tool use agent, and multi-agent chat. Experimental results show that APPL can effectively parallelize independent LLM calls, achieving a significant speedup ratio. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary APPL is a new way to work with Large Language Models (LLMs). Right now, using these models can be tricky and take a lot of effort. APPL helps by letting you write simple programs in Python that work well with the models. This makes it easier to use the models for lots of different tasks. The system also has tools to help you figure out what went wrong if something doesn’t work as planned. We showed how APPL works with three examples, and tests prove that it can make things faster by working on multiple tasks at once. |
Keywords
» Artificial intelligence » Language model » Prompt » Syntax
