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LLMs struggle to improve dialogue parsing via input rewriting

By PulseAugur Editorial · [2 sources] ·

Researchers explored input rewriting techniques using Large Language Models (LLMs) to improve dialogue discourse parsing, particularly in scenarios where supervised clarification is unavailable. Their findings indicate that parser-agnostic rewriting often introduces more errors than corrections, and even parser-aware methods with GRPO show limitations in consistently improving parsing accuracy. The study highlights the need for a rewritability prediction capability to determine if an utterance can be repaired before intervention, which is crucial for optimizing frozen discourse parsers and enhancing agentic pipelines. AI

IMPACT Highlights limitations in current LLM capabilities for complex NLP tasks, suggesting new research directions for agentic systems.

RANK_REASON Academic paper detailing a novel approach to dialogue discourse parsing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs struggle to improve dialogue parsing via input rewriting

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Yiming Liu, Ziyue Zhang, Zhichao Xu, Xin Yu, Yingheng Tang, Tianyu Jiang, Jie Cao ·

    Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing

    arXiv:2607.01964v1 Announce Type: new Abstract: Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that supervised clarification models can rewrite fragmentary…

  2. arXiv cs.CL TIER_1 English(EN) · Jie Cao ·

    Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing

    Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse parsing (DDP) shows that supervised clarification models can rewrite fragmentary or underspecified utterances, such as resolving…

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