While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We analyze these failures and identify Softmax, the scoring function in the attention mechanism, as a contributing factor. We propose Scaled Signed Averaging (SSA), a novel attention scoring function that mitigates these failures. SSA significantly improves performance on our ICL tasks and outperforms transformer models with Softmax on several NLP benchmarks and linguistic probing tasks, in both decoder-only and encoder-only architectures.

Mainstream multilingual LLMs are generally trained on a much higher proportion of English than multilingual data, raising questions about their ability to capture linguistic features particular to non-English languages or to capture information important to non-anglophone cultures. We add to a growing effort to increase multilingual sensitivity in LLMs by developing a benchmark, EIFFEL, testing mastery of French idiomatic expressions in context. We fully explain the methodology, which exploits input from native French speakers, to make it reproducible for other languages. We compare mainstream multilingual LLMs with French-focused LLMs both on standard LLM benchmarks and EIFFEL; EIFFEL brings out the benefits of higher proportions of French data and shows limitations of standard benchmarks for measuring multilingual competence.

Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attribution methods and concept-based explanations. The library bridges recent research and practical tooling by exposing explanation workflows through a unified API for both classification and text generation. A key differentiator is its end-to-end concept-based pipeline (from activation extraction to concept learning, interpretation, and scoring), which goes beyond feature-level attributions and is uncommon in existing libraries.