@inproceedings{gunzler-etal-2024-sovereign,
title = {S{\"o}vereign at The Perspective Argument Retrieval Shared Task 2024: Using {LLM}s with Argument Mining},
author = {G{\"u}nzler, Robert and
Sevgili, {\"O}zge and
Remus, Steffen and
Biemann, Chris and
Nikishina, Irina},
editor = "Ajjour, Yamen and
Bar-Haim, Roy and
El Baff, Roxanne and
Liu, Zhexiong and
Skitalinskaya, Gabriella",
booktitle = "Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.argmining-1.15",
pages = "150--158",
abstract = {This paper presents the S{\"o}vereign submission for the shared task on perspective argument retrieval for the Argument Mining Workshop 2024. The main challenge is to perform argument retrieval considering socio-cultural aspects such as political interests, occupation, age, and gender. To address the challenge, we apply open-access Large Language Models (Mistral-7b) in a zero-shot fashion for re-ranking and explicit similarity scoring. Additionally, we combine different features in an ensemble setup using logistic regression. Our system ranks second in the competition for all test set rounds on average for the logistic regression approach using LLM similarity scores as a feature. In addition to the description of the approach, we also provide further results of our ablation study. Our code will be open-sourced upon acceptance.},
}
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<abstract>This paper presents the Sövereign submission for the shared task on perspective argument retrieval for the Argument Mining Workshop 2024. The main challenge is to perform argument retrieval considering socio-cultural aspects such as political interests, occupation, age, and gender. To address the challenge, we apply open-access Large Language Models (Mistral-7b) in a zero-shot fashion for re-ranking and explicit similarity scoring. Additionally, we combine different features in an ensemble setup using logistic regression. Our system ranks second in the competition for all test set rounds on average for the logistic regression approach using LLM similarity scores as a feature. In addition to the description of the approach, we also provide further results of our ablation study. Our code will be open-sourced upon acceptance.</abstract>
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%0 Conference Proceedings
%T Sövereign at The Perspective Argument Retrieval Shared Task 2024: Using LLMs with Argument Mining
%A Günzler, Robert
%A Sevgili, Özge
%A Remus, Steffen
%A Biemann, Chris
%A Nikishina, Irina
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F gunzler-etal-2024-sovereign
%X This paper presents the Sövereign submission for the shared task on perspective argument retrieval for the Argument Mining Workshop 2024. The main challenge is to perform argument retrieval considering socio-cultural aspects such as political interests, occupation, age, and gender. To address the challenge, we apply open-access Large Language Models (Mistral-7b) in a zero-shot fashion for re-ranking and explicit similarity scoring. Additionally, we combine different features in an ensemble setup using logistic regression. Our system ranks second in the competition for all test set rounds on average for the logistic regression approach using LLM similarity scores as a feature. In addition to the description of the approach, we also provide further results of our ablation study. Our code will be open-sourced upon acceptance.
%U https://aclanthology.org/2024.argmining-1.15
%P 150-158
Markdown (Informal)
[Sövereign at The Perspective Argument Retrieval Shared Task 2024: Using LLMs with Argument Mining](https://aclanthology.org/2024.argmining-1.15) (Günzler et al., ArgMining 2024)
ACL