We propose DecoderLens, a method to evaluate the iterative refinement of representations in encoder-decoder Transformer models.
We introduce PECoRe, an interpretability framework for identifying context dependence in language model generations.
We introduce Retrieval and Attribute-Marking enhanced Prompting (RAMP) to perform attribute-controlled MT with multilingual LLMs.
We analyze input contributions of char-level MT models and show how they modulate word and character-level information.
We present Inseq, a Python library to democratize access to interpretability analyses of sequence generation models.
DivEMT is a publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.