4 papers · 1 filter
Scaling Inherently Interpretable Language Models
Guide Labs Team, Andreas Madsen, Aya Abdelsalam Ismail +7
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods whose reliability is difficult…
New Faithfulness-Centric Interpretability Paradigms for Natural Language Processing
Andreas Madsen
As machine learning becomes more widespread and is used in more critical applications, it's important to provide explanations for these models, to prevent unintended behavior. Unfo…
Faithfulness Measurable Masked Language Models
Andreas Madsen, Siva Reddy, Sarath Chandar
A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong…
Are self-explanations from Large Language Models faithful?
Andreas Madsen, Sarath Chandar, Siva Reddy
Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanatio…