Leveraging Large Language Models to Build and Execute Computational
Workflows
- URL: http://arxiv.org/abs/2312.07711v1
- Date: Tue, 12 Dec 2023 20:17:13 GMT
- Title: Leveraging Large Language Models to Build and Execute Computational
Workflows
- Authors: Alejandro Duque, Abdullah Syed, Kastan V. Day, Matthew J. Berry,
Daniel S. Katz, Volodymyr V. Kindratenko
- Abstract summary: This paper explores how these emerging capabilities can be harnessed to facilitate complex scientific research.
We present initial findings from our attempt to integrate Phyloflow with OpenAI's function-calling API, and outline a strategy for developing a comprehensive workflow management system.
- Score: 40.572754656757475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The recent development of large language models (LLMs) with multi-billion
parameters, coupled with the creation of user-friendly application programming
interfaces (APIs), has paved the way for automatically generating and executing
code in response to straightforward human queries. This paper explores how
these emerging capabilities can be harnessed to facilitate complex scientific
workflows, eliminating the need for traditional coding methods. We present
initial findings from our attempt to integrate Phyloflow with OpenAI's
function-calling API, and outline a strategy for developing a comprehensive
workflow management system based on these concepts.
Related papers
- Semantic API Alignment: Linking High-level User Goals to APIs [6.494714497852088]
We present a vision to span multiple steps from requirements engineering to implementation using existing libraries.
This approach, which we call Semantic API Alignment (SEAL), aims to bridge the gap between a user's high-level goals and the specific functions of one or more APIs.
arXiv Detail & Related papers (2024-05-07T11:54:32Z) - Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems.
We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
arXiv Detail & Related papers (2024-05-07T04:00:30Z) - Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learning [14.351476383642016]
We propose a novel approach, named Code2API, to automatically perform APIzation for Stack Overflow code snippets.
Code2API does not require additional model training or any manual crafting rules.
It can be easily deployed on personal computers without relying on other external tools.
arXiv Detail & Related papers (2024-05-06T14:22:17Z) - A Framework to Model ML Engineering Processes [1.9744907811058787]
Development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets.
Current process modeling languages are not suitable for describing the development of such systems.
We introduce a framework for modeling ML-based software development processes, built around a domain-specific language.
arXiv Detail & Related papers (2024-04-29T09:17:36Z) - From Summary to Action: Enhancing Large Language Models for Complex
Tasks with Open World APIs [62.496139001509114]
We introduce a novel tool invocation pipeline designed to control massive real-world APIs.
This pipeline mirrors the human task-solving process, addressing complicated real-life user queries.
Empirical evaluations of our Sum2Act pipeline on the ToolBench benchmark show significant performance improvements.
arXiv Detail & Related papers (2024-02-28T08:42:23Z) - Octopus: Embodied Vision-Language Programmer from Environmental Feedback [59.772904419928054]
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning.
In this paper, we introduce Octopus, a novel VLM designed to proficiently decipher an agent's vision and textual task objectives.
Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games.
arXiv Detail & Related papers (2023-10-12T17:59:58Z) - Enhancing API Documentation through BERTopic Modeling and Summarization [0.0]
This paper focuses on the complexities of interpreting Application Programming Interface (API) documentation.
Official API documentation serves as a primary source of information for developers, but it can often be extensive and lacks user-friendliness.
Our novel approach employs the strengths of BERTopic for topic modeling and Natural Language Processing (NLP) to automatically generate summaries of API documentation.
arXiv Detail & Related papers (2023-08-17T15:57:12Z) - A Composable Just-In-Time Programming Framework with LLMs and FBP [0.0]
This paper introduces a computing framework that combines Flow-Based Programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time Programming (JITP)
JITP empowers users, regardless of their programming expertise, to actively participate in the development and automation process by leveraging their task-time algorithmic insights.
The framework allows users to request and generate code in real-time, enabling dynamic code execution within a flow-based program.
arXiv Detail & Related papers (2023-07-31T23:51:46Z) - Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
This paper introduces a novel human-LLM interaction framework, Low-code LLM.
It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses.
We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
arXiv Detail & Related papers (2023-04-17T09:27:40Z) - OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
Human Intelligence (HI) excels at combining basic skills to solve complex tasks.
This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents.
We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
arXiv Detail & Related papers (2023-04-10T03:55:35Z)
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.