Evaluating Prompt-Based and Fine-Tuned Approaches to Czech Anaphora Resolution

• URL: http://arxiv.org/abs/2506.18091v1
• Date: Sun, 22 Jun 2025 16:32:57 GMT
• Title: Evaluating Prompt-Based and Fine-Tuned Approaches to Czech Anaphora Resolution
• Authors: Patrik Stano, Aleš Horák,
• Abstract summary: Anaphora resolution plays a critical role in natural language understanding in morphologically rich languages like Czech.<n>This paper presents a comparative evaluation of two modern approaches to anaphora resolution on Czech text.<n>We compare prompt engineering with large language models (LLMs) and fine-tuning compact generative models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Anaphora resolution plays a critical role in natural language understanding, especially in morphologically rich languages like Czech. This paper presents a comparative evaluation of two modern approaches to anaphora resolution on Czech text: prompt engineering with large language models (LLMs) and fine-tuning compact generative models. Using a dataset derived from the Prague Dependency Treebank, we evaluate several instruction-tuned LLMs, including Mistral Large 2 and Llama 3, using a series of prompt templates. We compare them against fine-tuned variants of the mT5 and Mistral models that we trained specifically for Czech anaphora resolution. Our experiments demonstrate that while prompting yields promising few-shot results (up to 74.5% accuracy), the fine-tuned models, particularly mT5-large, outperform them significantly, achieving up to 88% accuracy while requiring fewer computational resources. We analyze performance across different anaphora types, antecedent distances, and source corpora, highlighting key strengths and trade-offs of each approach.

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