Machine translation systems still make systematic errors on idioms and other expressions that cannot be translated literally. Model editing offers a possible way to fix such recurring errors without retraining the whole model, but it has mostly been studied for monolingual factual updates rather than translation. We introduce MEMT-bench, the first benchmark for evaluating model editing in machine translation across multiple languages, models, and editing settings. The benchmark is built from idiomatic translation errors and tests whether an edit can correct a targeted mistranslation while preserving unrelated translation behavior. Experiments with causal tracing and representative editing methods reveal both the promise and difficulty of translation editing: edits can repair targeted errors, but maintaining cross-lingual locality and robustness under repeated edits remains challenging. Code and data are publicly available at [GitHub](https://github.com/dannalily/MEMT-Model-Editing-for-Targeted-Translation-Error-Repair/) and [Hugging Face](https://huggingface.co/datasets/Dannalily/MEMT-bench), respectively.

Information alignment evaluators are vital for various NLG evaluation tasks and trustworthy LLM deployment, reducing hallucinations and enhancing user trust. Current fine-grained methods, like FactScore, verify facts individually but neglect inter-fact dependencies, enabling subtle vulnerabilities.In this work, we introduce MontageLie, a challenging benchmark that constructs deceptive narratives by “montaging” truthful statements without introducing explicit hallucinations.We demonstrate that both coarse-grained LLM-based evaluators and current fine-grained frameworks are susceptible to this attack, with AUC-ROC scores falling below 65%.To enable more robust fine-grained evaluation, we propose DoveScore, a novel framework that jointly verifies factual accuracy and event-order consistency. By modeling inter-fact relationships, DoveScore outperforms existing fine-grained methods by over 8%, providing a more robust solution for long-form text alignment evaluation. Our code and datasets are available at https://github.com/dannalily/DoveScore.

We present Archer, a challenging bilingual text-to-SQL dataset specific to complex reasoning, including arithmetic, commonsense and hypothetical reasoning. It contains 1,042 English questions and 1,042 Chinese questions, along with 521 unique SQL queries, covering 20 English databases across 20 domains. Notably, this dataset demonstrates a significantly higher level of complexity compared to existing publicly available datasets. Our evaluation shows that Archer challenges the capabilities of current state-of-the-art models, with a high-ranked model on the Spider leaderboard achieving only 6.73% execution accuracy on Archer test set. Thus, Archer presents a significant challenge for future research in this field.