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It is shown that useful explanations can extend beyond the model a verbalizer was trained to describe and show that pretrained verbalizers recover words that models have learned to conceal through fine-tuning, without additional verbalizer training.
A temporal alignment failure, stale-document poisoning, is identified, in which outdated evidence makes a model wrong despite answering correctly without retrieval, and the same internal components also support broader comparison tasks, suggesting that temporal applicability can recruit a general reasoning mechanism us...
Daisy, a factual knowledge benchmark for Danish cultural heritage, based on curated topics from the Danish Culture Canon 2006, is introduced, with results showing that contemporary language models perform surprisingly poorly on this task.
A strongly typed domain-specific language with Haskell-like syntax, that enables a write-once, run everywhere paradigm for LLM architectures, Axon fosters open cooperation and empowers researchers to implement highly specialized architectures without giving up optimization infrastructure or accepting deployment lock-in...
The results show that prompt optimization techniques enable SLMs to handle out-of-distribution queries gracefully, without changing the models'weights, while weaker models may still need weight-level adaptation or schema-aware training.
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