ChatLogo: A Large Language Model-Driven Hybrid Natural-Programming
Language Interface for Agent-based Modeling and Programming
- URL: http://arxiv.org/abs/2308.08102v1
- Date: Wed, 16 Aug 2023 02:21:52 GMT
- Title: ChatLogo: A Large Language Model-Driven Hybrid Natural-Programming
Language Interface for Agent-based Modeling and Programming
- Authors: John Chen, Uri Wilensky
- Abstract summary: ChatLogo is a hybrid natural-programming language interface for agent-based modeling and programming.
ChatLogo aims to support conversations with computers in a mix of natural and programming languages.
- Score: 5.648811213672019
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Building on Papert (1980)'s idea of children talking to computers, we propose
ChatLogo, a hybrid natural-programming language interface for agent-based
modeling and programming. We build upon previous efforts to scaffold ABM & P
learning and recent development in leveraging large language models (LLMs) to
support the learning of computational programming. ChatLogo aims to support
conversations with computers in a mix of natural and programming languages,
provide a more user-friendly interface for novice learners, and keep the
technical system from over-reliance on any single LLM. We introduced the main
elements of our design: an intelligent command center, and a conversational
interface to support creative expression. We discussed the presentation format
and future work. Responding to the challenges of supporting open-ended
constructionist learning of ABM & P and leveraging LLMs for educational
purposes, we contribute to the field by proposing the first constructionist
LLM-driven interface to support computational and complex systems thinking.
Related papers
- CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - Natural Language based Context Modeling and Reasoning for Ubiquitous
Computing with Large Language Models: A Tutorial [35.743576799998564]
Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-aware computing.
In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning.
arXiv Detail & Related papers (2023-09-24T00:15:39Z) - PwR: Exploring the Role of Representations in Conversational Programming [17.838776812138626]
We introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language.
We find that representations significantly improve understandability, and instilled a sense of agency among our participants.
arXiv Detail & Related papers (2023-09-18T05:38:23Z) - ChatDev: Communicative Agents for Software Development [84.90400377131962]
ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate.
These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
arXiv Detail & Related papers (2023-07-16T02:11:34Z) - ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented
Instruction Tuning for Digital Human [76.62897301298699]
ChatPLUG is a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
We show that modelname outperforms state-of-the-art Chinese dialogue systems on both automatic and human evaluation.
We deploy modelname to real-world applications such as Smart Speaker and Instant Message applications with fast inference.
arXiv Detail & Related papers (2023-04-16T18:16:35Z) - An Overview on Language Models: Recent Developments and Outlook [32.528770408502396]
Conventional language models (CLMs) aim to predict the probability of linguistic sequences in a causal manner.
Pre-trained language models (PLMs) cover broader concepts and can be used in both causal sequential modeling and fine-tuning for downstream applications.
arXiv Detail & Related papers (2023-03-10T07:55:00Z) - A Case Study in Engineering a Conversational Programming Assistant's
Persona [72.47187215119664]
Conversational capability was achieved by using an existing code-fluent Large Language Model.
A discussion of the evolution of the prompt provides a case study in how to coax an existing foundation model to behave in a desirable manner for a particular application.
arXiv Detail & Related papers (2023-01-13T14:48:47Z) - PanGu-Coder: Program Synthesis with Function-Level Language Modeling [47.63943623661298]
PanGu-Coder is a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation.
We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modelling to pre-train on raw programming language data.
The second stage uses a combination of Causal Language Modelling and Masked Language Modelling to train on loosely curated pairs of natural language program definitions and code functions.
arXiv Detail & Related papers (2022-07-22T18:08:16Z) - A Conversational Paradigm for Program Synthesis [110.94409515865867]
We propose a conversational program synthesis approach via large language models.
We train a family of large language models, called CodeGen, on natural language and programming language data.
Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm.
arXiv Detail & Related papers (2022-03-25T06:55:15Z)
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.