13 citations · 18 across the 15 of their papers we have counts for
16 papers
Adapting Linear-Time Architectures for Tabular In-Context Learning
David Schnurr, Felix Sarnthein, Thomas Hofmann +1
Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large datasets. Existing linear-time…
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
Tiberiu Musat, Tiago Pimentel, Nicolas Zucchet +1
We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics h…
Fast and Geometrically Grounded Lorentz Neural Networks
Robert van der Klis, Ricardo Chávez Torres, Max van Spengler +3
Hyperbolic space is quickly gaining traction as a promising geometry for hierarchical and robust representation learning. A core open challenge is the development of a mathematical…
Heads collapse, features stay: Why Replay needs big buffers
Giulia Lanzillotta, Damiano Meier, Thomas Hofmann
A persistent paradox in continual learning (CL) is that neural networks often retain linearly separable representations of past tasks even when their output predictions fail. We fo…
On the Emergence of Induction Heads for In-Context Learning
Tiberiu Musat, Tiago Pimentel, Lorenzo Noci +3
Transformers have become the dominant architecture for natural language processing. Part of their success is owed to a remarkable capability known as in-context learning (ICL): the…
Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning
Lina Berrayana, Ahmed Heakl, Muhammad Abdullah Sohail +3
Current autoregressive language models (ARMs) achieve high accuracy but require long token sequences, making them costly. Discrete diffusion language models (DDLMs) enable parallel…
The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?
Denis Sutter, Julian Minder, Thomas Hofmann +1
The concept of causal abstraction got recently popularised to demystify the opaque decision-making processes of machine learning models; in short, a neural network can be abstracte…
Causal Estimation of Tokenisation Bias
Pietro Lesci, Clara Meister, Thomas Hofmann +2
Modern language models are typically trained over subword sequences, but ultimately define probabilities over character-strings. Ideally, the choice of the tokeniser -- which maps…
Optimality of Right-Invariant Priors
Jannis Bolik, Thomas Hofmann
We discuss optimal prediction for families of probability distributions with a locally compact topological group structure. Right-invariant priors were previously shown to yield a…
Local and Global Decoding in Text Generation
Daniel Gareev, Thomas Hofmann, Ezhilmathi Krishnasamy +1
Text generation, a key component in applications such as dialogue systems, relies on decoding algorithms that sample strings from a language model distribution. Traditional methods…
Explicit Word Density Estimation for Language Modelling
Jovan Andonov, Octavian Ganea, Paulina Grnarova +2
Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for c…
Causal Estimation of Memorisation Profiles
Pietro Lesci, Clara Meister, Thomas Hofmann +2
Understanding memorisation in language models has practical and societal implications, e.g., studying models' training dynamics or preventing copyright infringements. Prior work de…
The Role of Language Imbalance in Cross-lingual Generalisation: Insights from Cloned Language Experiments
Anton Schäfer, Shauli Ravfogel, Thomas Hofmann +2
Multilinguality is crucial for extending recent advancements in language modelling to diverse linguistic communities. To maintain high performance while representing multiple langu…
On the Effect of (Near) Duplicate Subwords in Language Modelling
Anton Schäfer, Thomas Hofmann, Imanol Schlag +1
Tokenisation is a core part of language models (LMs). It involves splitting a character sequence into subwords which are assigned arbitrary indices before being served to the LM. W…
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
Aleksandar Stanić, Dylan Ashley, Oleg Serikov +5
The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field…
Starting Small -- Learning with Adaptive Sample Sizes
Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
For many machine learning problems, data is abundant and it may be prohibitive to make multiple passes through the full training set. In this context, we investigate strategies for…