3 citations · 6 across the 10 of their papers we have counts for
10 papers
Fine-Tuned In-Context Learners for Efficient Adaptation
Jorg Bornschein, Clare Lyle, Yazhe Li +3
When adapting large language models (LLMs) to a specific downstream task, two primary approaches are commonly employed: (1) prompt engineering, often with in-context few-shot learn…
On the generalization of language models from in-context learning and finetuning: a controlled study
Andrew K. Lampinen, Arslan Chaudhry, Stephanie C. Y. Chan +7
Large language models exhibit exciting capabilities, yet can show surprisingly narrow generalization from finetuning. E.g. they can fail to generalize to simple reversals of relati…
LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Thomas Schmied, Jörg Bornschein, Jordi Grau-Moya +2
The success of Large Language Models (LLMs) has sparked interest in various agentic applications. A key hypothesis is that LLMs, leveraging common sense and Chain-of-Thought (CoT)…
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet, Jörg Bornschein, Stephanie Chan +3
Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynam…
Language Model Embeddings Can Be Sufficient for Bayesian Optimization
Tung Nguyen, Qiuyi Zhang, Bangding Yang +6
Bayesian Optimization is ubiquitous in experimental design and black-box optimization for improving search efficiency. However, most existing approaches rely on regression models w…
Imitating Language via Scalable Inverse Reinforcement Learning
Markus Wulfmeier, Michael Bloesch, Nino Vieillard +13
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning…
Transformers for Supervised Online Continual Learning
Jorg Bornschein, Yazhe Li, Amal Rannen-Triki
Transformers have become the dominant architecture for sequence modeling tasks such as natural language processing or audio processing, and they are now even considered for tasks t…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
When Does Re-initialization Work?
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim +4
Re-initializing a neural network during training has been observed to improve generalization in recent works. Yet it is neither widely adopted in deep learning practice nor is it o…
A study on the plasticity of neural networks
Tudor Berariu, Wojciech Czarnecki, Soham De +4
One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task. Usually t…