May 2026 focused on stabilizing and clarifying evaluation workflows in NVIDIA/NeMo-Skills. Implemented a critical COMET evaluation bug fix that corrects the reference key usage from 'gt' to 'ref', enhancing accuracy, stability, and reproducibility of COMET-based metrics. This change improves confidence in evaluation results used for model comparisons and product decisions, and aligns with ongoing quality improvements in the evaluation pipeline.

January 2026: Delivered enhancements to the machine translation evaluation pipeline in NVIDIA/NeMo-Skills, adding COMET metric support and multi-sample BLEU for Korean and Japanese, along with required tokenization package installations and aggregation of BLEU and COMET scores across multiple predictions. Two commits implemented the work: 448e97b3bb781970a5b224a84771817315e16ee4 ("Comet metrics for machine translation (#1156)") and a8cfe4358a0ca932d16049ec404a306f35862361 ("Multi-sample MT sacrebleu support for ko/ja (#1179)"). This improves evaluation fidelity, enables better model comparison, and speeds data-driven decisions for MT quality improvements.

Month 2025-10 summary for NVIDIA/NeMo-Skills focusing on multilingual evaluation expansion. Implemented enhanced multilingual evaluation by adding FLORES200 and WMT24pp datasets, updating benchmarks, metrics, and prompt configurations to enable more comprehensive translation evaluation. Documented changes and prepared evaluation scaffolding for broader model assessment.

In 2025-03, focused on hardening Char Tokenizer in NVIDIA/NeMo to improve security and robustness. Fixed a vulnerability by removing dynamic eval usage and implementing safe token parsing (ASCII-encoded then decoded or direct character extraction when no escape sequence). This reduces the risk of code execution from crafted tokens and strengthens production reliability.