1 citations · 4 across the 27 of their papers we have counts for
8 papers · 1 filter
Disentangling the Expressivity of RoPE
Selim Jerad, Anej Svete, Jiaoda Li +1
Two accounts recur in explanations of the success of rotary position embeddings (RoPE). Expressivity studies associate periodic position information with modular predicates, wherea…
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers
Ying Fan, Anej Svete, Kangwook Lee
Language models typically reason via explicit chain-of-thought (CoT), generating intermediate steps token-by-token. Latent CoT offers an alternative: it performs multi-step reasoni…
Efficiently Representing Algorithms With Chain-of-Thought Transformers
Yanhong Li, Anej Svete, Ashish Sabharwal +1
The increasing popularity of \emph{reasoning} models -- language models that output a series of reasoning or thought tokens before producing an answer -- is justified, in part, by…
Causally Evaluating the Learnability of Formal Language Tasks
Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda +3
Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. A…
Understanding the Parameter Space Geometry of Transformers Encoding Boolean Functions
Blanka Köver, Alexandra Butoi, Anej Svete +2
Transformers consistently fail to learn certain simple functions that are provably expressible with specific parameter settings. This gap between learnability and expressivity is p…
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
Anej Svete, William Merrill, Ryan Cotterell +1
Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizations and are sensitive to model…
Olmo Hybrid: From Theory to Practice and Back
William Merrill, Yanhong Li, Tyler Romero +19
Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attentio…
Context-Free Recognition with Transformers
Selim Jerad, Anej Svete, Sophie Hao +2
Transformers excel empirically on tasks that process well-formed inputs according to some grammar, such as natural language and code. However, it remains unclear how they can proce…