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LLMs struggle with inferential narrative features in Turkish corpus study

By PulseAugur Editorial · [1 sources] ·

A new study evaluated the inter-rater reliability of large language models (LLMs) and rule-based systems in annotating inferential narrative features within a Turkish corpus. The research found that models like Gemini 2.5 Flash, Grok, Claude Fable-5, and ChatGPT 5.5, along with a rule-based detector, showed low agreement with human annotators on features such as materialized metaphor. The study suggests that these inferential features may be too complex for current automatic detection or that the definitions themselves are not yet operational enough for consistent application by any rater. AI

IMPACT Highlights limitations of current LLMs in nuanced text analysis, suggesting a gap in understanding complex inferential features.

RANK_REASON Academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with inferential narrative features in Turkish corpus study

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Academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Levent Bulut ·

    Inter-Rater Reliability of LLM and Rule-Based Annotation for Inferential Narrative Features: Three Studies on a Turkish Corpus

    arXiv:2609.13936v1 Announce Type: new Abstract: Datasets that ship automatically generated feature annotations invite a question rarely asked of them: would a human agree with those labels? This report answers that for the Objective Projection corpus, a Turkish narrative dataset …

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