@inproceedings{dmonte-etal-2025-exploring,
title = "Exploring the Performance of Large Language Models on Subjective Span Identification Tasks",
author = "Dmonte, Alphaeus and
Oruche, Roland R and
Ranasinghe, Tharindu and
Zampieri, Marcos and
Calyam, Prasad",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-short.30/",
pages = "358--371",
ISBN = "979-8-89176-299-2",
abstract = "Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification approaches rely on relatively smaller pre-trained language models like BERT, a few recent approaches have leveraged the latest generation of Large Language Models (LLMs) for the task. Current work has focused on explicit span identification like Named Entity Recognition (NER), while more subjective span identification with LLMs in tasks like Aspect-based Sentiment Analysis (ABSA) has been underexplored. In this paper, we fill this important gap by presenting an evaluation of the performance of various LLMs on text span identification in three popular tasks, namely sentiment analysis, offensive language identification, and claim verification. We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought. Our results indicate underlying relationships within text aid LLMs in identifying precise text spans."
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%0 Conference Proceedings
%T Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
%A Dmonte, Alphaeus
%A Oruche, Roland R.
%A Ranasinghe, Tharindu
%A Zampieri, Marcos
%A Calyam, Prasad
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-299-2
%F dmonte-etal-2025-exploring
%X Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification approaches rely on relatively smaller pre-trained language models like BERT, a few recent approaches have leveraged the latest generation of Large Language Models (LLMs) for the task. Current work has focused on explicit span identification like Named Entity Recognition (NER), while more subjective span identification with LLMs in tasks like Aspect-based Sentiment Analysis (ABSA) has been underexplored. In this paper, we fill this important gap by presenting an evaluation of the performance of various LLMs on text span identification in three popular tasks, namely sentiment analysis, offensive language identification, and claim verification. We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought. Our results indicate underlying relationships within text aid LLMs in identifying precise text spans.
%U https://aclanthology.org/2025.ijcnlp-short.30/
%P 358-371
Markdown (Informal)
[Exploring the Performance of Large Language Models on Subjective Span Identification Tasks](https://aclanthology.org/2025.ijcnlp-short.30/) (Dmonte et al., IJCNLP-AACL 2025)
ACL
- Alphaeus Dmonte, Roland R Oruche, Tharindu Ranasinghe, Marcos Zampieri, and Prasad Calyam. 2025. Exploring the Performance of Large Language Models on Subjective Span Identification Tasks. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 358–371, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.