6 papers
Latent Planning Emerges with Scale
Michael Hanna, Emmanuel Ameisen
LLMs can perform seemingly planning-intensive tasks, like writing coherent stories or functioning code, without explicitly verbalizing a plan; however, the extent to which they imp…
Are formal and functional linguistic mechanisms dissociated in language models?
Michael Hanna, Yonatan Belinkov, Sandro Pezzelle
Although large language models (LLMs) are increasingly capable, these capabilities are unevenly distributed: they excel at formal linguistic tasks, such as producing fluent, gramma…
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe +20
How can we know whether new mechanistic interpretability methods achieve real improvements? In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretabili…
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…
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…