@inproceedings{morozov-etal-2025-empaths,
title = "Empaths at {S}em{E}val-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction",
author = "Morozov, Lev and
Mogilevskii, Aleksandr and
Shirnin, Alexander",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.259/",
pages = "2000--2007",
ISBN = "979-8-89176-273-2",
abstract = "The paper introduces EmoRAG, a retrieval-augmented emotion detection system designed for the SemEval-2025 Task 11. It uses an ensemble of models, retrieving similar examples to prompt large language models (LLMs) for emotion predictions. The retriever component fetches the most relevant examples from a database, which are then used as few-shot prompts for the models. EmoRAG achieves strong, scalable performance across languages with no training at all, demonstrating effectiveness in both high and low-resource languages."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="morozov-etal-2025-empaths">
<titleInfo>
<title>Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lev</namePart>
<namePart type="family">Morozov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aleksandr</namePart>
<namePart type="family">Mogilevskii</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Shirnin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Rosenthal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aiala</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Zampieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-273-2</identifier>
</relatedItem>
<abstract>The paper introduces EmoRAG, a retrieval-augmented emotion detection system designed for the SemEval-2025 Task 11. It uses an ensemble of models, retrieving similar examples to prompt large language models (LLMs) for emotion predictions. The retriever component fetches the most relevant examples from a database, which are then used as few-shot prompts for the models. EmoRAG achieves strong, scalable performance across languages with no training at all, demonstrating effectiveness in both high and low-resource languages.</abstract>
<identifier type="citekey">morozov-etal-2025-empaths</identifier>
<location>
<url>https://aclanthology.org/2025.semeval-1.259/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>2000</start>
<end>2007</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction
%A Morozov, Lev
%A Mogilevskii, Aleksandr
%A Shirnin, Alexander
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F morozov-etal-2025-empaths
%X The paper introduces EmoRAG, a retrieval-augmented emotion detection system designed for the SemEval-2025 Task 11. It uses an ensemble of models, retrieving similar examples to prompt large language models (LLMs) for emotion predictions. The retriever component fetches the most relevant examples from a database, which are then used as few-shot prompts for the models. EmoRAG achieves strong, scalable performance across languages with no training at all, demonstrating effectiveness in both high and low-resource languages.
%U https://aclanthology.org/2025.semeval-1.259/
%P 2000-2007
Markdown (Informal)
[Empaths at SemEval-2025 Task 11: Retrieval-Augmented Approach to Perceived Emotions Prediction](https://aclanthology.org/2025.semeval-1.259/) (Morozov et al., SemEval 2025)
ACL