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

• URL: http://arxiv.org/abs/2601.00736v1
• Date: Fri, 02 Jan 2026 16:30:14 GMT
• Title: Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
• Authors: Alphaeus Dmonte, Roland Oruche, Tharindu Ranasinghe, Marcos Zampieri, Prasad Calyam,
• Abstract summary: This paper presents an evaluation of various Large Language Models (LLMs) on text span identification in three popular NLP tasks.<n>We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought.<n>Our results indicate underlying relationships within text aid LLMs in identifying precise text spans.
• Score: 21.931193759012725
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text [4.753395401707132]
  DA-MTL is a multi-task learning framework that simultaneously addresses text detection and authorship attribution.<n>We evaluate DA-MTL on nine datasets and four backbone models, demonstrating its strong performance across multiple languages.
  arXiv  Detail & Related papers  (2025-08-19T18:23:30Z)
• Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-Sample Aggregation on Large Language Models [52.829293635314194]
  Keyphrase generation is a long-standing NLP task for automatically generating keyphrases for a given document.<n>We focus on the zero-shot capabilities of open-source instruction-tuned LLMs (Phi-3, Llama-3) and the closed-source GPT-4o for this task.
  arXiv  Detail & Related papers  (2025-03-01T19:38:57Z)
• BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages [93.92804151830744]
  We present BRIGHTER, a collection of multi-labeled, emotion-annotated datasets in 28 different languages.<n>We highlight the challenges related to the data collection and annotation processes.<n>We show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition.
  arXiv  Detail & Related papers  (2025-02-17T15:39:50Z)
• Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
  We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information. We evaluate four state-of-the-art large language models on MLNeedle.
  arXiv  Detail & Related papers  (2024-08-19T17:02:06Z)
• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT [9.682499180341273]
  Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges.<n>We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English.<n>This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning [41.606494950216764]
  In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs) This paper decomposes the overall performance of ICL into three dimensions, label space, format, and discrimination. We show that ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
  arXiv  Detail & Related papers  (2024-04-11T08:20:10Z)
• Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models [54.58989938395976]
  We introduce a decomposed prompting approach for sequence labeling tasks.<n>We test our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages.
  arXiv  Detail & Related papers  (2024-02-28T15:15:39Z)
• Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
  This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:45:25Z)
• SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks [88.4408774253634]
  Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community. There are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark for several tasks.
  arXiv  Detail & Related papers  (2022-12-20T18:39:59Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)
• A Survey on Recent Advances in Sequence Labeling from Deep Learning Models [19.753741555478793]
  Sequence labeling is a fundamental research problem encompassing a variety of tasks. Deep learning has been employed for sequence labeling tasks due to its powerful capability in automatically learning complex features.
  arXiv  Detail & Related papers  (2020-11-13T02:29:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.