7 papers
Semantic Adapter Routing with Fine-Tuning Task Embeddings
Enrico Cassano, MichaÅ Brzozowski, Michał Brzozowski +3
Parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. Given such a library, routing aims to s…
Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing
MichaÅ Brzozowski, Zuzanna Dubanowska, Enrico Cassano +1
Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weights or training data, remains…
Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)
MichaÅ Brzozowski, Neo Christopher Chung
Sparse autoencoders (SAEs) are one of the main methods to interpret the inner workings of deep neural networks (DNNs), decomposing activations into higher-dimensional features. How…
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
MichaÅ Brzozowski, Neo Christopher Chung
These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academic co-authors across hundreds…
Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design
MichaÅ Brzozowski, Neo Christopher Chung
Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces polysemanticity. However, f…
GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning
Paolo Mandica, MichaÅ Brzozowski, Zuzanna Dubanowska +1
Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEFT) of large language models (LLMs). However, its bilinear structure introduces a…