LangProBe: a Language Programs Benchmark

• URL: http://arxiv.org/abs/2502.20315v1
• Date: Thu, 27 Feb 2025 17:41:49 GMT
• Title: LangProBe: a Language Programs Benchmark
• Authors: Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, Matei Zaharia,
• Abstract summary: We introduce LangProBe, the first large-scale benchmark for evaluating the architectures and optimization strategies for language programs.<n>We find that optimized language programs offer strong cost--quality improvement over raw calls to models, but simultaneously demonstrate that human judgment is still necessary for best performance.
• Score: 53.81811700561928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Composing language models (LMs) into multi-step language programs and automatically optimizing their modular prompts is now a mainstream paradigm for building AI systems, but the tradeoffs in this space have only scarcely been studied before. We introduce LangProBe, the first large-scale benchmark for evaluating the architectures and optimization strategies for language programs, with over 2000 combinations of tasks, architectures, optimizers, and choices of LMs. Using LangProBe, we are the first to study the impact of program architectures and optimizers (and their compositions together and with different models) on tradeoffs of quality and cost. We find that optimized language programs offer strong cost--quality Pareto improvement over raw calls to models, but simultaneously demonstrate that human judgment (or empirical decisions) about which compositions to pursue is still necessary for best performance. We will open source the code and evaluation data for LangProBe.

Related papers

• Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention [71.12193680015622]
  Large Language Models (LLMs) have shown remarkable capabilities in natural language processing. LLMs exhibit significant performance gaps among different languages. We propose Inference-Time Cross-Lingual Intervention (INCLINE) to overcome these limitations without incurring significant costs.
  arXiv  Detail & Related papers  (2024-10-16T11:23:03Z)
• Multi-Programming Language Ensemble for Code Generation in Large Language Model [5.882816711878273]
  Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. Most existing approaches focus solely on generating code in a single programming language, overlooking the potential of leveraging the multi-language capabilities of LLMs. We propose Multi-Programming Language Ensemble (MPLE), a novel ensemble-based method that utilizes code generation across multiple programming languages to enhance overall performance.
  arXiv  Detail & Related papers  (2024-09-06T08:31:18Z)
• Should AI Optimize Your Code? A Comparative Study of Current Large Language Models Versus Classical Optimizing Compilers [0.0]
  Large Language Models (LLMs) raise intriguing questions about the potential for AI-driven approaches to revolutionize code optimization methodologies. This paper presents a comparative analysis between two state-of-the-art Large Language Models, GPT-4.0 and CodeLlama-70B, and traditional optimizing compilers.
  arXiv  Detail & Related papers  (2024-06-17T23:26:41Z)
• Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs [40.159064885288245]
  We study prompt optimization for Language Model Programs. We factorize our problem into optimizing the free-form instructions and few-shot demonstrations of every module. We develop MIPRO, a novel algorithm for optimizing LM programs.
  arXiv  Detail & Related papers  (2024-06-17T16:12:03Z)
• LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language [23.692367748537517]
  We propose LangGPT, a dual-layer prompt design framework as the programming language for LLMs. LangGPT has an easy-to-learn normative structure and provides an extended structure for migration and reuse.
  arXiv  Detail & Related papers  (2024-02-26T15:05:16Z)
• Leveraging Reinforcement Learning and Large Language Models for Code Optimization [14.602997316032706]
  This paper introduces a new framework to decrease the complexity of code optimization. The proposed framework builds on large language models (LLMs) and reinforcement learning (RL) We run several experiments on the PIE dataset using a CodeT5 language model and RRHF, a new reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2023-12-09T19:50:23Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• Learning Performance-Improving Code Edits [107.21538852090208]
  We introduce a framework for adapting large language models (LLMs) to high-level program optimization. First, we curate a dataset of performance-improving edits made by human programmers of over 77,000 competitive C++ programming submission pairs. For prompting, we propose retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
  arXiv  Detail & Related papers  (2023-02-15T18:59:21Z)
• Natural Language to Code Translation with Execution [82.52142893010563]
  Execution result--minimum Bayes risk decoding for program selection. We show that it improves the few-shot performance of pretrained code models on natural-language-to-code tasks.
  arXiv  Detail & Related papers  (2022-04-25T06:06:08Z)
• Learning to Superoptimize Real-world Programs [79.4140991035247]
  We propose a framework to learn to superoptimize real-world programs by using neural sequence-to-sequence models. We introduce the Big Assembly benchmark, a dataset consisting of over 25K real-world functions mined from open-source projects in x86-64 assembly.
  arXiv  Detail & Related papers  (2021-09-28T05:33:21Z)
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis [16.520971531754018]
  We introduce ProGraML, a graph-based program representation for machine learning. ProGraML achieves an average 94.0 F1 score, significantly outperforming the state-of-the-art approaches. We then apply our approach to two high-level tasks - heterogeneous device mapping and program classification - setting new state-of-the-art performance in both.
  arXiv  Detail & Related papers  (2020-03-23T20:27:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.