<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rush-2023-minichain">
    <titleInfo>
        <title>MiniChain: A Small Library for Coding with Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Rush</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yansong</namePart>
            <namePart type="family">Feng</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Els</namePart>
            <namePart type="family">Lefever</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Programming augmented by large language models (LLMs) opens up many new application areas, but also requires care. LLMs are accurate enough, on average, to replace core functionality, yet make basic mistakes that demonstrate a lack of robustness. An ecosystem of prompting tools, from intelligent agents to new programming languages, have emerged with different solutions for patching LLMs with other tools. In this work, we introduce MiniChain, an opinionated tool for LLM augmented programming, with the design goals of ease-of-use of prototyping, transparency through automatic visualization, and a minimalistic approach to advanced features. The MiniChain library provides core primitives for coding LLM calls, separating out prompt templates, and capturing program structure. The library includes demo implementations of the main applications papers in the area, including chat-bots, code generation, retrieval-based question answering, and complex information extraction. The library is open-source and available at https://github.com/srush/MiniChain, with code demos available at https://srush-minichain.hf.space/, and video demo at https://www.youtube.com/watch?v=VszZ1VnO7sk.</abstract>
    <identifier type="citekey">rush-2023-minichain</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-demo.27</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-demo.27/</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>311</start>
            <end>317</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MiniChain: A Small Library for Coding with Large Language Models
%A Rush, Alexander
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F rush-2023-minichain
%X Programming augmented by large language models (LLMs) opens up many new application areas, but also requires care. LLMs are accurate enough, on average, to replace core functionality, yet make basic mistakes that demonstrate a lack of robustness. An ecosystem of prompting tools, from intelligent agents to new programming languages, have emerged with different solutions for patching LLMs with other tools. In this work, we introduce MiniChain, an opinionated tool for LLM augmented programming, with the design goals of ease-of-use of prototyping, transparency through automatic visualization, and a minimalistic approach to advanced features. The MiniChain library provides core primitives for coding LLM calls, separating out prompt templates, and capturing program structure. The library includes demo implementations of the main applications papers in the area, including chat-bots, code generation, retrieval-based question answering, and complex information extraction. The library is open-source and available at https://github.com/srush/MiniChain, with code demos available at https://srush-minichain.hf.space/, and video demo at https://www.youtube.com/watch?v=VszZ1VnO7sk.
%R 10.18653/v1/2023.emnlp-demo.27
%U https://aclanthology.org/2023.emnlp-demo.27/
%U https://doi.org/10.18653/v1/2023.emnlp-demo.27
%P 311-317

Markdown (Informal)

[MiniChain: A Small Library for Coding with Large Language Models](https://aclanthology.org/2023.emnlp-demo.27/) (Rush, EMNLP 2023)

• MiniChain: A Small Library for Coding with Large Language Models (Rush, EMNLP 2023)

ACL

• Alexander Rush. 2023. MiniChain: A Small Library for Coding with Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 311–317, Singapore. Association for Computational Linguistics.