North Small Translate is a new open-weight translation model that also follows instructions, described as being trained on the same base as Cohere's Command A Plus mixture-of-experts system with 25 billion active parameters. The authors position it as a cost-effective option for machine translation workloads that need instruction-following behaviour.
A new arXiv preprint examines prompt echoing, a failure mode where an instruct-tuned language model repeats the user's input instead of answering, even without being told to do so. The work focuses on smaller instruct models and asks what this behavior reveals about how they process and respond to instructions.
A new arXiv paper introduces IBBench-Light, an evaluation that presents the same external record to a model under two different uses: as a procedure the model must carry out, or as text it must simply read. Each of twelve semantic bases produces 144 matched response pairs per model, and four quantized instruction-tuned models were tested. The paired setup is meant to isolate whether models react to a directive's form or to the user's stated task.
A new arXiv paper introduces MTAC-IFBench, a benchmark aimed at measuring how well large language model agents follow instructions across multi-turn coding sessions. The work targets agentic software engineering, where models plan, run code, and call external tools over successive steps rather than producing a single answer. It addresses evaluation beyond functional correctness, focusing on whether agents keep to the constraints given to them.
A new arXiv paper examines whether instruction tuning gives language models a general-purpose ability to follow instructions or whether the behavior emerges from coordinated, task-specific components. The authors argue the evidence points to skillful coordination rather than one universal mechanism. The work adds to ongoing debate about what instruction tuning actually changes inside a model.
A new arXiv paper investigates whether foundation models can apply complex content moderation policies reliably and consistently. The study compares two ways of translating moderation rules into model behavior: conveying them through explicit instructions versus through illustrative examples. The findings are relevant to platforms seeking scalable, automated moderation of online content.