@inproceedings{temesgen-etal-2026-exemplar,
title = "Exemplar Choice Matters for Morphological Reasoning: Model Confidence Vs Exemplar Similarity",
author = "Temesgen, Tsedeniya Kinfe and
Marco, Marion Di and
Fraser, Alexander",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1941/",
pages = "41382--41410",
ISBN = "979-8-89176-462-0",
abstract = "LLMs have shown impressive linguistic competencies in a wide range of NLP tasks, yet morphological generalization remains challenging. Models can benefit from in-context learning, where the choice of few-shot examples significantly influences the outcome. We probe LLMs' morphological capabilities by assessing how two targeted in-context exemplar selection strategies, Iterative Confidence-Based Exemplar Selection (i-CES) and Similarity-Based Exemplar Selection (SES), influence the model{'}s ability for inflectional and derivational modeling across multiple languages. Our experiments with LLaMA 3.3-70B-instruct and Qwen2.5-32B-Instruct models indicate that SES is more effective for accessing latent linguistic knowledge, showing a substantial performance advantage over i-CES in both derivational and inflectional tasks. In contrast, i-CES offers no clear advantage over random sampling."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="temesgen-etal-2026-exemplar">
<titleInfo>
<title>Exemplar Choice Matters for Morphological Reasoning: Model Confidence Vs Exemplar Similarity</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tsedeniya</namePart>
<namePart type="given">Kinfe</namePart>
<namePart type="family">Temesgen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marion</namePart>
<namePart type="given">Di</namePart>
<namePart type="family">Marco</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Fraser</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>LLMs have shown impressive linguistic competencies in a wide range of NLP tasks, yet morphological generalization remains challenging. Models can benefit from in-context learning, where the choice of few-shot examples significantly influences the outcome. We probe LLMs’ morphological capabilities by assessing how two targeted in-context exemplar selection strategies, Iterative Confidence-Based Exemplar Selection (i-CES) and Similarity-Based Exemplar Selection (SES), influence the model’s ability for inflectional and derivational modeling across multiple languages. Our experiments with LLaMA 3.3-70B-instruct and Qwen2.5-32B-Instruct models indicate that SES is more effective for accessing latent linguistic knowledge, showing a substantial performance advantage over i-CES in both derivational and inflectional tasks. In contrast, i-CES offers no clear advantage over random sampling.</abstract>
<identifier type="citekey">temesgen-etal-2026-exemplar</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.1941/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>41382</start>
<end>41410</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Exemplar Choice Matters for Morphological Reasoning: Model Confidence Vs Exemplar Similarity
%A Temesgen, Tsedeniya Kinfe
%A Marco, Marion Di
%A Fraser, Alexander
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F temesgen-etal-2026-exemplar
%X LLMs have shown impressive linguistic competencies in a wide range of NLP tasks, yet morphological generalization remains challenging. Models can benefit from in-context learning, where the choice of few-shot examples significantly influences the outcome. We probe LLMs’ morphological capabilities by assessing how two targeted in-context exemplar selection strategies, Iterative Confidence-Based Exemplar Selection (i-CES) and Similarity-Based Exemplar Selection (SES), influence the model’s ability for inflectional and derivational modeling across multiple languages. Our experiments with LLaMA 3.3-70B-instruct and Qwen2.5-32B-Instruct models indicate that SES is more effective for accessing latent linguistic knowledge, showing a substantial performance advantage over i-CES in both derivational and inflectional tasks. In contrast, i-CES offers no clear advantage over random sampling.
%U https://aclanthology.org/2026.findings-emnlp.1941/
%P 41382-41410
Markdown (Informal)
[Exemplar Choice Matters for Morphological Reasoning: Model Confidence Vs Exemplar Similarity](https://aclanthology.org/2026.findings-emnlp.1941/) (Temesgen et al., Findings 2026)
ACL