@inproceedings{chiyah-garcia-etal-2024-adapting,
title = "Adapting {LLM} Predictions in In-Context Learning with Data Priors",
author = "Chiyah-Garcia, Javier and
Goyal, Prasoon and
Johnston, Michael and
Ghanadan, Reza",
editor = "Kumar, Sachin and
Balachandran, Vidhisha and
Park, Chan Young and
Shi, Weijia and
Hayati, Shirley Anugrah and
Tsvetkov, Yulia and
Smith, Noah and
Hajishirzi, Hannaneh and
Kang, Dongyeop and
Jurgens, David",
booktitle = "Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.customnlp4u-1.23",
doi = "10.18653/v1/2024.customnlp4u-1.23",
pages = "305--316",
abstract = "In-Context Learning (ICL) has enabled Large Language Models (LLMs) to excel as general-purpose models in zero and few-shot task settings. However, since LLMs are often not trained on the downstream tasks, they lack crucial contextual knowledge from the data distributions, which limits their task adaptability.This paper explores using data priors to automatically customize prompts in ICL. We extract these priors in a dataset-agnostic way basedon historical information, enabling LLMs to personalize their output towards users or tasks at inference time. We find that they improve LLM{'}s output by injecting latent dataset-specific information for the task of rating prediction. Throughout a series of experiments, we show replicable results across LLMs and datasets on what information and methods are most effective for adapting ICL outputs with priors. Our findings offer a systematic approach to customizing prompts with additional information in a privacy-friendly manner, requiring only aggregated data that is computationally efficient.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chiyah-garcia-etal-2024-adapting">
<titleInfo>
<title>Adapting LLM Predictions in In-Context Learning with Data Priors</title>
</titleInfo>
<name type="personal">
<namePart type="given">Javier</namePart>
<namePart type="family">Chiyah-Garcia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Prasoon</namePart>
<namePart type="family">Goyal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Johnston</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Reza</namePart>
<namePart type="family">Ghanadan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sachin</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vidhisha</namePart>
<namePart type="family">Balachandran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chan</namePart>
<namePart type="given">Young</namePart>
<namePart type="family">Park</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weijia</namePart>
<namePart type="family">Shi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shirley</namePart>
<namePart type="given">Anugrah</namePart>
<namePart type="family">Hayati</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulia</namePart>
<namePart type="family">Tsvetkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Noah</namePart>
<namePart type="family">Smith</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hannaneh</namePart>
<namePart type="family">Hajishirzi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dongyeop</namePart>
<namePart type="family">Kang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Miami, Florida, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In-Context Learning (ICL) has enabled Large Language Models (LLMs) to excel as general-purpose models in zero and few-shot task settings. However, since LLMs are often not trained on the downstream tasks, they lack crucial contextual knowledge from the data distributions, which limits their task adaptability.This paper explores using data priors to automatically customize prompts in ICL. We extract these priors in a dataset-agnostic way basedon historical information, enabling LLMs to personalize their output towards users or tasks at inference time. We find that they improve LLM’s output by injecting latent dataset-specific information for the task of rating prediction. Throughout a series of experiments, we show replicable results across LLMs and datasets on what information and methods are most effective for adapting ICL outputs with priors. Our findings offer a systematic approach to customizing prompts with additional information in a privacy-friendly manner, requiring only aggregated data that is computationally efficient.</abstract>
<identifier type="citekey">chiyah-garcia-etal-2024-adapting</identifier>
<identifier type="doi">10.18653/v1/2024.customnlp4u-1.23</identifier>
<location>
<url>https://aclanthology.org/2024.customnlp4u-1.23</url>
</location>
<part>
<date>2024-11</date>
<extent unit="page">
<start>305</start>
<end>316</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Adapting LLM Predictions in In-Context Learning with Data Priors
%A Chiyah-Garcia, Javier
%A Goyal, Prasoon
%A Johnston, Michael
%A Ghanadan, Reza
%Y Kumar, Sachin
%Y Balachandran, Vidhisha
%Y Park, Chan Young
%Y Shi, Weijia
%Y Hayati, Shirley Anugrah
%Y Tsvetkov, Yulia
%Y Smith, Noah
%Y Hajishirzi, Hannaneh
%Y Kang, Dongyeop
%Y Jurgens, David
%S Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chiyah-garcia-etal-2024-adapting
%X In-Context Learning (ICL) has enabled Large Language Models (LLMs) to excel as general-purpose models in zero and few-shot task settings. However, since LLMs are often not trained on the downstream tasks, they lack crucial contextual knowledge from the data distributions, which limits their task adaptability.This paper explores using data priors to automatically customize prompts in ICL. We extract these priors in a dataset-agnostic way basedon historical information, enabling LLMs to personalize their output towards users or tasks at inference time. We find that they improve LLM’s output by injecting latent dataset-specific information for the task of rating prediction. Throughout a series of experiments, we show replicable results across LLMs and datasets on what information and methods are most effective for adapting ICL outputs with priors. Our findings offer a systematic approach to customizing prompts with additional information in a privacy-friendly manner, requiring only aggregated data that is computationally efficient.
%R 10.18653/v1/2024.customnlp4u-1.23
%U https://aclanthology.org/2024.customnlp4u-1.23
%U https://doi.org/10.18653/v1/2024.customnlp4u-1.23
%P 305-316
Markdown (Informal)
[Adapting LLM Predictions in In-Context Learning with Data Priors](https://aclanthology.org/2024.customnlp4u-1.23) (Chiyah-Garcia et al., CustomNLP4U 2024)
ACL
- Javier Chiyah-Garcia, Prasoon Goyal, Michael Johnston, and Reza Ghanadan. 2024. Adapting LLM Predictions in In-Context Learning with Data Priors. In Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), pages 305–316, Miami, Florida, USA. Association for Computational Linguistics.