TY - GEN

T1 - Argument Quality Assessment in the Age of Instruction-Following Large Language Models

AU - Wachsmuth, Henning

AU - Lapesa, Gabriella

AU - Cabrio, Elena

AU - Lauscher, Anne

AU - Park, Joonsuk

AU - Vecchi, Eva Maria

AU - Villata, Serena

AU - Ziegenbein, Timon

N1 - © 2024 ELRA Language Resource Association

PY - 2024/5

Y1 - 2024/5

N2 - The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument’s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

AB - The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument’s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

U2 - 10.48550/arXiv.2403.16084

DO - 10.48550/arXiv.2403.16084

M3 - Conference contribution

SP - 1519

EP - 1538

BT - Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

A2 - Calzolari, Nicoletta

A2 - Kan, Min-Yen

A2 - Hoste, Veronique

A2 - Lenci, Alessandro

A2 - Sakti, Sakriani

A2 - Xue, Nianwen

T2 - Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

Y2 - 20 May 2024 through 25 May 2024

ER -