@inproceedings{porwal-etal-2025-analysis,
title = "Analysis of {LLM} as a grammatical feature tagger for {A}frican {A}merican {E}nglish",
author = "Porwal, Rahul and
Rozet, Alice and
Gowda, Jotsna and
Houck, Pryce and
Tang, Kevin and
Moeller, Sarah",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.431/",
doi = "10.18653/v1/2025.findings-naacl.431",
pages = "7744--7756",
ISBN = "979-8-89176-195-7",
abstract = "African American English (AAE) presents unique challenges in natural language processing (NLP) This research systematically compares the performance of available NLP models{---}rule-based, transformer-based, and large language models (LLMs){---}capable of identifying key grammatical features of AAE, namely Habitual Be and Multiple Negation. These features were selected for their distinct grammatical complexity and frequency of occurrence. The evaluation involved sentence-level binary classification tasks, using both zero-shot and few-shot strategies. The analysis reveals that while LLMs show promise compared to the baseline, they are influenced by biases such as recency and unrelated features in the text such as formality. This study highlights the necessity for improved model training and architectural adjustments to better accommodate AAE{'}s unique linguistic characteristics. Data and code are available."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="porwal-etal-2025-analysis">
<titleInfo>
<title>Analysis of LLM as a grammatical feature tagger for African American English</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rahul</namePart>
<namePart type="family">Porwal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alice</namePart>
<namePart type="family">Rozet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jotsna</namePart>
<namePart type="family">Gowda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pryce</namePart>
<namePart type="family">Houck</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Tang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sarah</namePart>
<namePart type="family">Moeller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: NAACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-195-7</identifier>
</relatedItem>
<abstract>African American English (AAE) presents unique challenges in natural language processing (NLP) This research systematically compares the performance of available NLP models—rule-based, transformer-based, and large language models (LLMs)—capable of identifying key grammatical features of AAE, namely Habitual Be and Multiple Negation. These features were selected for their distinct grammatical complexity and frequency of occurrence. The evaluation involved sentence-level binary classification tasks, using both zero-shot and few-shot strategies. The analysis reveals that while LLMs show promise compared to the baseline, they are influenced by biases such as recency and unrelated features in the text such as formality. This study highlights the necessity for improved model training and architectural adjustments to better accommodate AAE’s unique linguistic characteristics. Data and code are available.</abstract>
<identifier type="citekey">porwal-etal-2025-analysis</identifier>
<identifier type="doi">10.18653/v1/2025.findings-naacl.431</identifier>
<location>
<url>https://aclanthology.org/2025.findings-naacl.431/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>7744</start>
<end>7756</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Analysis of LLM as a grammatical feature tagger for African American English
%A Porwal, Rahul
%A Rozet, Alice
%A Gowda, Jotsna
%A Houck, Pryce
%A Tang, Kevin
%A Moeller, Sarah
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F porwal-etal-2025-analysis
%X African American English (AAE) presents unique challenges in natural language processing (NLP) This research systematically compares the performance of available NLP models—rule-based, transformer-based, and large language models (LLMs)—capable of identifying key grammatical features of AAE, namely Habitual Be and Multiple Negation. These features were selected for their distinct grammatical complexity and frequency of occurrence. The evaluation involved sentence-level binary classification tasks, using both zero-shot and few-shot strategies. The analysis reveals that while LLMs show promise compared to the baseline, they are influenced by biases such as recency and unrelated features in the text such as formality. This study highlights the necessity for improved model training and architectural adjustments to better accommodate AAE’s unique linguistic characteristics. Data and code are available.
%R 10.18653/v1/2025.findings-naacl.431
%U https://aclanthology.org/2025.findings-naacl.431/
%U https://doi.org/10.18653/v1/2025.findings-naacl.431
%P 7744-7756
Markdown (Informal)
[Analysis of LLM as a grammatical feature tagger for African American English](https://aclanthology.org/2025.findings-naacl.431/) (Porwal et al., Findings 2025)
ACL