14 citations · 19 across the 11 of their papers we have counts for
5 papers · 1 filter
Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models
Michael Hanna, Aaron Mueller
Autoregressive transformer language models (LMs) possess strong syntactic abilities, often successfully handling phenomena from agreement to NPI licensing. However, the features th…
LLM Circuit Analyses Are Consistent Across Training and Scale
Curt Tigges, Michael Hanna, Qinan Yu +1
Most currently deployed large language models (LLMs) undergo continuous training or additional finetuning. By contrast, most research into LLMs' internal mechanisms focuses on mode…
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi +17
There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reprodu…
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
Michael Hanna, Sandro Pezzelle, Yonatan Belinkov
Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM be…
Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!
Frank Wildenburg, Michael Hanna, Sandro Pezzelle
In everyday language use, speakers frequently utter and interpret sentences that are semantically underspecified, namely, whose content is insufficient to fully convey their messag…