Exploring the Word Sense Disambiguation Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2503.08662v1
• Date: Tue, 11 Mar 2025 17:50:44 GMT
• Title: Exploring the Word Sense Disambiguation Capabilities of Large Language Models
• Authors: Pierpaolo Basile, Lucia Siciliani, Elio Musacchio, Giovanni Semeraro,
• Abstract summary: We evaluate the performance of various Large Language Models (LLMs) on the Word Sense Disambiguation task.<n>The results indicate that LLMs perform well in zero-shot learning but cannot surpass current state-of-the-art methods.
• Score: 2.9998889086656586
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Word Sense Disambiguation (WSD) is a historical task in computational linguistics that has received much attention over the years. However, with the advent of Large Language Models (LLMs), interest in this task (in its classical definition) has decreased. In this study, we evaluate the performance of various LLMs on the WSD task. We extend a previous benchmark (XL-WSD) to re-design two subtasks suitable for LLM: 1) given a word in a sentence, the LLM must generate the correct definition; 2) given a word in a sentence and a set of predefined meanings, the LLM must select the correct one. The extended benchmark is built using the XL-WSD and BabelNet. The results indicate that LLMs perform well in zero-shot learning but cannot surpass current state-of-the-art methods. However, a fine-tuned model with a medium number of parameters outperforms all other models, including the state-of-the-art.

Related papers

• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs) We find that fine-tuning existing text embedding models on LLM-generated texts yields excellent classification accuracy. We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups [1.8377902806196766]
  Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5-turbo and GPT-4o, in the CWI, LCP, and MWE settings.
  arXiv  Detail & Related papers  (2024-11-03T22:31:02Z)
• Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints [20.844061807562436]
  We propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks.
  arXiv  Detail & Related papers  (2024-09-22T14:35:09Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics [74.99898531299148]
  This research examines vocabulary trimming (VT) inspired by restricting embedding entries to the language of interest to bolster time and memory efficiency. We apply two languages to trim the full vocabulary - Unicode-based script filtering and corpus-based selection - to different language families and sizes. It is found that VT reduces the memory usage of small models by nearly 50% and has an upper bound of 25% improvement in generation speed.
  arXiv  Detail & Related papers  (2023-11-16T09:35:50Z)
• LLM-augmented Preference Learning from Natural Language [19.700169351688768]
  Large Language Models (LLMs) are equipped to deal with larger context lengths. LLMs can consistently outperform the SotA when the target text is large. Few-shot learning yields better performance than zero-shot learning.
  arXiv  Detail & Related papers  (2023-10-12T17:17:27Z)
• Scaling Sentence Embeddings with Large Language Models [43.19994568210206]
  In this work, we propose an in-context learning-based method aimed at improving sentence embeddings performance. Our approach involves adapting the previous prompt-based representation method for autoregressive models. By scaling model size, we find scaling to more than tens of billion parameters harms the performance on semantic textual similarity tasks.
  arXiv  Detail & Related papers  (2023-07-31T13:26:03Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)
• Translate to Disambiguate: Zero-shot Multilingual Word Sense Disambiguation with Pretrained Language Models [67.19567060894563]
  Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be finetuned to perform well on diverse tasks. We present a new study investigating how well PLMs capture cross-lingual word sense with Contextual Word-Level Translation (C-WLT) We find that as the model size increases, PLMs encode more cross-lingual word sense knowledge and better use context to improve WLT performance.
  arXiv  Detail & Related papers  (2023-04-26T19:55:52Z)

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.