Adaptive Self-improvement LLM Agentic System for ML Library Development

• URL: http://arxiv.org/abs/2502.02534v1
• Date: Tue, 04 Feb 2025 17:57:17 GMT
• Title: Adaptive Self-improvement LLM Agentic System for ML Library Development
• Authors: Genghan Zhang, Weixin Liang, Olivia Hsu, Kunle Olukotun,
• Abstract summary: Large language models (LLMs) have shown general coding capabilities. LLMs need complex reasoning with limited data in order to complete this task. We introduce an adaptive self-improvement agentic system to generate ASPL code with both open and closed-source LLMs.
• Score: 8.766639641127412
• License:
• Abstract: ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing these high-performance ML libraries is challenging because it requires expert knowledge of ML algorithms and the ASPL. Large language models (LLMs), on the other hand, have shown general coding capabilities. However, challenges remain when using LLMs for generating ML libraries using ASPLs because 1) this task is complicated even for experienced human programmers and 2) there are limited code examples because of the esoteric and evolving nature of ASPLs. Therefore, LLMs need complex reasoning with limited data in order to complete this task. To address these challenges, we introduce an adaptive self-improvement agentic system. In order to evaluate the effectiveness of our system, we construct a benchmark of a typical ML library and generate ASPL code with both open and closed-source LLMs on this benchmark. Our results show improvements of up to $3.9\times$ over a baseline single LLM.

Related papers

• Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation [2.249533649156367]
  Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets. We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination.
  arXiv  Detail & Related papers  (2024-11-22T14:27:27Z)
• SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  SpecTool is a new benchmark to identify error patterns in LLM output on tool-use tasks. We show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience [4.814313782484443]
  Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks. We use LLMs to generate domain-specific data, thereby improving the performance of pre-trained LMs on the target tasks.
  arXiv  Detail & Related papers  (2024-08-16T06:37:59Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends [30.774685501251817]
  General large language models (LLMs) have demonstrated significant potential in tasks such as code generation in software engineering. A considerable portion of Code LLMs is derived from general LLMs through model fine-tuning. There is currently a lack of systematic investigation into Code LLMs and their performance.
  arXiv  Detail & Related papers  (2023-11-17T07:55:16Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• Evaluating In-Context Learning of Libraries for Code Generation [35.57902679044737]
  Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. Recent work has shown that large proprietary LLMs can learn novel library usage in-context from demonstrations.
  arXiv  Detail & Related papers  (2023-11-16T07:37:25Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.