Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908744486486016 |
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| author | Dmonte, Alphaeus Oruche, Roland Ranasinghe, Tharindu Zampieri, Marcos Calyam, Prasad |
| author_facet | Dmonte, Alphaeus Oruche, Roland Ranasinghe, Tharindu Zampieri, Marcos Calyam, Prasad |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_00736 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploring the Performance of Large Language Models on Subjective Span Identification Tasks Dmonte, Alphaeus Oruche, Roland Ranasinghe, Tharindu Zampieri, Marcos Calyam, Prasad Computation and Language Artificial Intelligence 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. |
| title | Exploring the Performance of Large Language Models on Subjective Span Identification Tasks |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.00736 |