Exploring the Performance of Large Language Models on Subjective Span Identification Tasks

2026-01-02

arXiv.org

AI Summary

This study addresses a critical gap in the literature by systematically evaluating the capacity of large language models (LLMs) to identify subjective text segments—a capability not previously assessed in a comprehensive manner. The work presents the first thorough examination of LLM performance across three representative tasks: sentiment analysis, offensive language detection, and claim verification. Leveraging a combination of instruction tuning, in-context learning, and chain-of-thought reasoning, the experiments demonstrate that LLMs effectively model intra-textual semantic relationships, substantially improving accuracy in segment-level subjectivity recognition. The findings underscore the pivotal role of leveraging textual structural information for fine-grained subjective understanding and establish both a methodological framework and empirical foundation for future research in this domain.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Large Language Models
• Subjective Span Identification
• Aspect-based Sentiment Analysis
• Offensive Language Identification
• Claim Verification

Innovation

• Large Language Models
• Subjective Span Identification
• In-Context Learning
• Instruction Tuning
• Chain of Thought