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 ellipsis or references—to improve parsing accuracy. In this work, we revisit this idea under realistic deployment conditions, where no clarification supervision is available and the clarifier must rely on zero-shot prompting or feedback from a frozen parser. Across three Segmented Discourse Representation Theory (SDRT) datasets and multiple parsers, we find that last-utterance clarification is far less reliable than suggested by supervised settings. Parser-agnostic rewriting often introduces more regressions than repairs, as edits that enable fixes also disrupt discourse cues relied upon by the parser. A best-of-8 rewriting analysis further reveals a practical ceiling: a large fraction of errors are not repairable through input rewriting alone. A parser-aware clarifier trained with GRPO reduces regressions by up to 37% by learning conservative abstention, yet still fails to produce selectivity-aware clarifications that consistently improve parsing. Together, these findings recast clarification as a selective intervention problem. We identify rewritability prediction—deciding whether an utterance is repairable before intervention—as the key missing capability for input-side optimization of frozen discourse parsers, and a critical direction for improving agentic pipelines more broadly.[Data and code are available at <https://github.com/ounlp/Clarification-for-DDP>.]

Incorporating Large Language Models (LLMs) for downstream tasks has recently garnered considerable attention, where fine-tuning plays a key role in LLMs’ adaptation. These LLMs, often consisting of billions of parameters, require vast amounts of computational resources when customizing them for new tasks. To mitigate this, researchers have proposed the parameter-efficient fine-tuning (PEFT) as a practical solution by adjusting fewer parameters of a pre-trained LLM. However, these methods heavily rely on their own structural modifications that fail to establish an efficient knowledge-sharing mechanism to distill rich knowledge from other expert models, which may lead to inefficient fine-tuning. In this paper, we propose Pen2Sword, a lightweight fine-tuning framework for domain adaptation which efficiently transfers knowledge from a small expert model to a target large model via embedding layers, significantly enhancing the fine-tuning efficiency of large models. Specifically, we first selects optimal expert models via a preserving function, then facilitates knowledge transfer through vocabulary alignment and embedding expansion, and finally accelerates domain adaptation with a fast fine-tuning paradigm. Extensive empirical evaluations across multiple domains demonstrate that our Pen2Sword framework consistently accelerates domain-specific fine-tuning, improves model performance (e.g., +13.6% in code and +20.1% in math), and remains robust across diverse model families and PEFT methods. The codes and data are available at https://github.com/pengmeishu/Pen2Sword.