6 papers
Discovering Interpretable Algorithms by Decompiling Transformers to RASP
Xinting Huang, Aleksandra Bakalova, Satwik Bhattamishra +2
Recent work has shown that the computations of Transformers can be simulated in the RASP family of programming languages. These findings have enabled improved understanding of the…
Decomposing Representation Space into Interpretable Subspaces with Unsupervised Learning
Xinting Huang, Michael Hahn
Understanding internal representations of neural models is a core interest of mechanistic interpretability. Due to its large dimensionality, the representation space can encode var…
Tug-of-war between idioms' figurative and literal interpretations in LLMs
Soyoung Oh, Xinting Huang, Mathis Pink +2
Idioms present a unique challenge for language models due to their non-compositional figurative interpretations, which often strongly diverge from the idiom's literal interpretatio…
Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B
Aleksandra Bakalova, Yana Veitsman, Xinting Huang +1
In-Context Learning (ICL) is an intriguing ability of large language models (LLMs). Despite a substantial amount of work on its behavioral aspects and how it emerges in miniature s…
Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers
Alireza Amiri, Xinting Huang, Mark Rofin +1
Chain-of-thought reasoning and scratchpads have emerged as critical tools for enhancing the computational capabilities of transformers. While theoretical results show that polynomi…
A Formal Framework for Understanding Length Generalization in Transformers
Xinting Huang, Andy Yang, Satwik Bhattamishra +5
A major challenge for transformers is generalizing to sequences longer than those observed during training. While previous works have empirically shown that transformers can either…