The paper introduces a method for text simplification that uses a Large Language Model-as-a-Judge (LLM‑Judge) to automatically generate policy‑aligned training data, eliminating the need for costly human annotation or parallel corpora. This approach allows simplification models to adapt to various policies—such as lexical‑focused simplification or overall rewriting that trades detail for simplicity—at both sentence and document levels. Experiments show that small‑scale open‑source LLMs can match or surpass GPT‑5, with consistent improvements across model families and sizes.
arXiv:2606. 00462v1 Announce Type: cross Abstract: Short-form text rewriting is a constrained variant of paraphrasing in which limited context and high semantic density leave little room for variation.
arXiv:2607. 25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields.
arXiv:2510.12306v4 Announce Type: replace Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic...
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
The paper explores how Large Language Models (LLMs) such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA can be used to automatically simplify complex medical texts into plain language, a process known as Plain Language Adaptation (PLA). It evaluates different prompting strategies, fine‑tunes models with QLoRA, and incorporates Mixture‑of‑Agents (MoA) techniques to improve adaptability and robustness. The results show that LLM‑driven PLA effectively makes healthcare information more comprehensible while preserving essential content.
arXiv:2610.10378v1 Announce Type: new Abstract: Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution...
arXiv:2505.16227v4 Announce Type: replace-cross Abstract: Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary ba...
The paper presents a pipeline that leverages large language models to extract grammatical rules, example sentences, and lexicons from descriptive grammar books, producing synthetic parallel corpora for fine‑tuning machine translation models. Evaluated on three low‑resource languages—Kalamang, Tuatschin, and Mandan—the synthetic data improves translation quality over seed‑data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, achieving up to +8.8 ChrF++ gains. A factorial study across 96 configurations identifies which combinations of target part‑of‑speech, retrieval granularity, and sample volume drive performance gains and where they fail, demonstrating that static linguistic documentation can be repurposed for practical translation tools for severely under‑resourced languages.
InComeS is a framework that improves large language models (LLMs) for model editing by compressing editing contexts into a key‑value cache associated with a special gist token. It adds cross‑attention modules to dynamically select the most relevant information from these gist pools, allowing efficient handling of multiple edits beyond the LLM’s context window. Experiments on diverse editing benchmarks show that InComeS enhances both effectiveness and efficiency compared to existing methods.
arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
DynEdit is a dynamic framework for editing long-sequence knowledge in large language models. It uses token-level entropy to guide non-uniform sliding windows, focusing on high-entropy, information-dense regions that are harder to edit. Experiments on three benchmarks and two backbone models show DynEdit outperforms baselines, achieving up to +14.0 BLEU points on challenging paraphrase queries and near-saturated semantic scores across diverse knowledge domains.
arXiv:2606. 16276v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but instead by provider- or application-specific model specifications.