TACTICS is a method for selecting evaluation samples in machine translation that explicitly optimizes for coverage of rare linguistic categories, document-level coherence, and distributional fidelity to the full corpus. It builds a hierarchical taxonomy from a locale style guide, classifies segments, and chooses a fixed-budget subset that better represents the full range of phenomena a system must handle. Compared to random, lexical, or embedding-based selection, TACTICS improves coverage of rare categories and yields more accurate system rankings with fewer segments.
arXiv:2609.25298v1 Announce Type: new Abstract: Cultural evaluation coverage and robustness in language models are difficult to diagnose because pretraining corpora and cultural benchmarks are rarely...
The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.
arXiv:2608. 10970v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment.
arXiv:2605.27268v2 Announce Type: replace-cross Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabu...
The paper presents a curated German–English parallel corpus derived from OPUS Books, reduced from 45,000 raw sentence pairs to 25,000 high‑quality examples through filtering and quality scoring. It details a reproducible fine‑tuning process for Meta’s NLLB‑200 models using mixed precision and gradient techniques, yielding a fine‑tuned NLLB‑600M model that improves BLEU, chrF++, and TER scores on literary and technical translations. The authors also integrate the model into the LinguaHaru translation system, enabling format‑preserving translation of PDFs and Office documents, and release all scripts, code, and the model on HuggingFace.
Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment. However, directly relying on LLM-generated expansions often leads to noisy, redundant, or hierarchically inconsistent structures, limiting their reliability for automated taxonomy expansion.
arXiv:2608.30614v1 Announce Type: new Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at m...
TransClean introduces a benchmark for identifying and removing translation noise—unwanted text such as language labels, explanations, or bilingual repetitions—from large language model (LLM) outputs. The authors analyzed 790,000 translations from 12 LLMs across 22 language pairs, cataloguing 12 common noise patterns and creating 9,900 noisy‑clean pairs (8,800 synthetic, 1,100 authentic). They evaluated two extraction methods—a span‑based approach using quality estimation models and an LLM‑prompted method—demonstrating the first systematic framework to assess and improve translation cleanliness.
arXiv:2610.27611v1 Announce Type: cross Abstract: Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing a...
The paper reports UvA-MT’s submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track for all 23 translation directions. The approach leverages test‑time methods of Large Language Models, using sequential sampling with a refinement prompt to generate multiple translation candidates and then employing LLMs as pairwise judges in a round‑robin voting scheme to select the best candidate. Offline evaluation on the WMT25 benchmark shows a clear improvement over traditional point‑wise best‑of‑N selection.
arXiv:2607. 19101v1 Announce Type: cross Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications.
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.