APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914840283447296 |
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| author | Dong, Honghua Su, Qidong Gao, Yubo Li, Zhaoyu Ruan, Yangjun Pekhimenko, Gennady Maddison, Chris J. Si, Xujie |
| author_facet | Dong, Honghua Su, Qidong Gao, Yubo Li, Zhaoyu Ruan, Yangjun Pekhimenko, Gennady Maddison, Chris J. Si, Xujie |
| contents | Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain. To address this challenge, we propose APPL, A Prompt Programming Language that acts as a bridge between computer programs and LLMs, allowing seamless embedding of prompts into Python functions, and vice versa. APPL provides an intuitive and Python-native syntax, an efficient parallelized runtime with asynchronous semantics, and a tracing module supporting effective failure diagnosis and replaying without extra costs. We demonstrate that APPL programs are intuitive, concise, and efficient through three representative scenarios: Chain-of-Thought with self-consistency (CoT-SC), ReAct tool use agent, and multi-agent chat. Experiments on three parallelizable workflows further show that APPL can effectively parallelize independent LLM calls, with a significant speedup ratio that almost matches the estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts Dong, Honghua Su, Qidong Gao, Yubo Li, Zhaoyu Ruan, Yangjun Pekhimenko, Gennady Maddison, Chris J. Si, Xujie Artificial Intelligence Computation and Language Machine Learning Programming Languages Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain. To address this challenge, we propose APPL, A Prompt Programming Language that acts as a bridge between computer programs and LLMs, allowing seamless embedding of prompts into Python functions, and vice versa. APPL provides an intuitive and Python-native syntax, an efficient parallelized runtime with asynchronous semantics, and a tracing module supporting effective failure diagnosis and replaying without extra costs. We demonstrate that APPL programs are intuitive, concise, and efficient through three representative scenarios: Chain-of-Thought with self-consistency (CoT-SC), ReAct tool use agent, and multi-agent chat. Experiments on three parallelizable workflows further show that APPL can effectively parallelize independent LLM calls, with a significant speedup ratio that almost matches the estimation. |
| title | APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts |
| topic | Artificial Intelligence Computation and Language Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2406.13161 |