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arXiv:2509. 07829v4 Announce Type: replace-cross Abstract: Literary translation has recently gained attention as a distinct and complex task in machine translation research, yet translation by small open models remains an open problem, particularly for low-resource languages such as Romanian.
The paper introduces BASSE, a multilingual meta‑evaluation dataset containing 2,040 human‑rated abstractive summaries produced manually or by five LLMs with four prompts. Annotators scored each summary on coherence, consistency, fluency, relevance, and 5W1H using a 5‑point Likert scale. Benchmarking shows proprietary LLM‑judge models best align with human judgments, followed by criteria‑specific automatic metrics, while open‑source judge LLMs perform poorly.
The paper presents a retrieval‑augmented generation (RAG) system combined with prompt engineering for Japanese‑to‑Chinese translation, focusing on sentences with noun‑modifying clause constructions (NMCCs). It integrates linguistic analysis, embedding‑based retrieval, and prompt construction before generating translations with GPT‑4o, without altering the base model. Experiments on a 66‑sentence test set show that increasing the knowledge‑base size from 0 to 2,000 examples raises macro‑averaged BLEU scores from 24.28 to 29.96, indicating improved translation quality for NMCC‑rich sentences.
arXiv:2606. 05924v1 Announce Type: cross Abstract: Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect.
arXiv:2609.18156v1 Announce Type: new Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...