9 papers
A Model with No Head and Many Thoughts
Nikita Koriagin, Yaroslav Aksenov, George Bredis +3
Large language models decode by projecting hidden states through a large vocabulary head at every step. This operation is computationally costly and forces all reasoning to be expr…
Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
Viacheslav Sinii, Nikita Balagansky, Gleb Gerasimov +6
The mechanisms by which reasoning training reshapes LLMs' internal computations remain unclear. We study lightweight steering vectors inserted into the base model's residual stream…
Teach Old SAEs New Domain Tricks with Boosting
Nikita Koriagin, Yaroslav Aksenov, Daniil Laptev +3
Sparse Autoencoders have emerged as powerful tools for interpreting the internal representations of Large Language Models, yet they often fail to capture domain-specific features n…
Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy
Nikita Balagansky, Yaroslav Aksenov, Daniil Laptev +4
Sparse Autoencoders (SAEs) have proven to be powerful tools for interpreting neural networks by decomposing hidden representations into disentangled, interpretable features via spa…
Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev +2
Sparse Autoencoders (SAEs) decompose language-model activations into sparse, interpretable features, but standard encoders usually treat the latent dictionary as a flat set of inde…
You Do Not Fully Utilize Transformer's Representation Capacity
Gleb Gerasimov, Yaroslav Aksenov, Nikita Balagansky +2
In contrast to RNNs, which compress their history into a single hidden state, Transformers can attend to all past tokens directly. However, standard Transformers rely solely on the…
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov +1
We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined…
Learn Your Reference Model for Real Good Alignment
Alexey Gorbatovski, Boris Shaposhnikov, Alexey Malakhov +5
Despite the fact that offline methods for Large Language Models (LLMs) alignment do not require a direct reward model, they remain susceptible to overoptimization. This issue arise…
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Yaroslav Aksenov, Nikita Balagansky, Sofia Maria Lo Cicero Vaina +3
Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, includi…