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Linguistic structure enrichment fails to improve LLM coherence assessment

By PulseAugur Editorial · [1 sources] ·

A new research paper explores whether adding syntactic and rhetorical information to text can improve the detection of incoherence in large language models. The study found that current model architectures were incompatible with this enriched data, leading to lower accuracy compared to plain text. However, the research also demonstrated that assessing textual coherence could be a useful proxy for identifying misleading content, as shown in experiments with a Brazilian disinformation dataset. Code and models for the study are publicly available. AI

IMPACT Current LLM architectures struggle with enhanced linguistic data, suggesting a need for architectural improvements to better understand and generate coherent text.

RANK_REASON Academic paper on LLM capabilities and evaluation. [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 →

Linguistic structure enrichment fails to improve LLM coherence assessment

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Academic paper on LLM capabilities and evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Victor Mazzotti, Luiz Pereira, Marina Bitencourt dos Santos, Helena Maia, Carlos Caetano, N\'adia Felix, Sandra Avila ·

    Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures

    arXiv:2609.10893v1 Announce Type: new Abstract: Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are grammatically correct but semantically incoherent, containing contradictions or d…

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