12 citations · 14 across the 10 of their papers we have counts for
4 papers · 1 filter
All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens
Siddarth Mamidanna, Daking Rai, Ziyu Yao +1
Large language models (LLMs) demonstrate proficiency across numerous computational tasks, yet their inner workings remain unclear. In theory, the combination of causal self-attenti…
Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
Daking Rai, Samuel Miller, Kevin Moran +1
Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanced parentheses. In this study, we inve…
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models
Dong Shu, Xuansheng Wu, Haiyan Zhao +4
Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted…
Mechanistic Understanding of Language Models in Syntactic Code Completion
Samuel Miller, Daking Rai, Ziyu Yao
Recently, language models (LMs) have shown impressive proficiency in code generation tasks, especially when fine-tuned on code-specific datasets, commonly known as Code LMs. Howeve…