arXiv Computation and Language September 15, 2026 By M\'at\'e Metzger

Can We Triage LLM Translation Errors in Classical Texts Without Human References? Source Novelty, GEMBA Scoring, and Budgeted Review through Pali-to-English Translation

Read the original on arXiv Computation and Language →

The Flow has not summarised this story yet — read it at arXiv Computation and Language.

arXiv Computation and Language

Sep 15

In the Blind: Building Pseudo-References for MT Evaluation

arXiv:2609.13611v1 Announce Type: new Abstract: The WMT26 General MT task evaluates systems on 10 language pairs that have no human references (neither translated from scratch nor post-edited from MT...

By Diptesh Kanojia, Chi-kiu Lo, Archchana Sindhujan, Samuel Larkin, Greg Hanneman, Alon Lavie

llms nlp

arXiv Computation and Language

6d ago

INVAR-Agri: Detecting Discrepancies in Multilingual Operator Manuals of Agricultural Implements with a Fine-Tuned LLM

arXiv:2610.35423v1 Announce Type: new Abstract: Operator manuals of agricultural implements are delivered in many languages, and the standards require that competent persons review every translation,...

By Timo Oksanen

llms fine-tuning

arXiv AI

Sep 1

Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects

arXiv:2608.28626v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This st...

By Emad Alharbi

llms multimodal

arXiv Computation and Language

Sep 10

AI translation of literary texts is "fine", but readers still prefer human translations

arXiv:2606.26040v2 Announce Type: replace Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...

By Yves Ferstler, Adam Podoxin, Ty Brassington, Ga\"elle Laperri\`ere, Roman Grundkiewicz, Marie-Jean Meurs, Maite Taboada, Marzena Karpinska

llms agents

Hugging Face Trending Papers

Oct 6

Making COMET Comparable Across Scripts: Diagnosis and Correction of Tokeniser-Induced Script Bias in Indic MT Evaluation

COMET reports translation quality as a single number, and that number is routinely compared across target languages written in different scripts. Such a comparison assumes Script Invariance: the score...

safety

arXiv Computation and Language

6d ago

Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems

arXiv:2610.33749v1 Announce Type: new Abstract: As machine translation systems approach human-level quality, traditional evaluation methodologies struggle to detect subtle translation errors. We crit...

By Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin

nlp benchmarks safety

One email a morning, machine-written

Related stories

In the Blind: Building Pseudo-References for MT Evaluation

INVAR-Agri: Detecting Discrepancies in Multilingual Operator Manuals of Agricultural Implements with a Fine-Tuned LLM

Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects

AI translation of literary texts is "fine", but readers still prefer human translations

Making COMET Comparable Across Scripts: Diagnosis and Correction of Tokeniser-Induced Script Bias in Indic MT Evaluation

Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems