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Found 7 papers, 3 papers with code
Can Large Language Models (or Humans) Distill Text?
We investigate the potential of large language models (LLMs) to distill text: to remove the textual traces of an undesired forbidden variable.
Reasoning in Transformers - Mitigating Spurious Correlations and Reasoning Shortcuts
Transformer language models are neural networks used for a wide variety of tasks concerning natural language, including some that also require logical reasoning.
The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models
In this work, we identify potential causes of inconsistency and evaluate the effectiveness of two mitigation strategies: up-scaling and augmenting the LM with a retrieval corpus.
Towards Learning Abstractions via Reinforcement Learning
In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning.
Conjectures, Tests and Proofs: An Overview of Theory Exploration
In this paper, we give a brief overview of a theory exploration system called QuickSpec, which is able to automatically discover interesting conjectures about a given set of functions.
Identifying cross country skiing techniques using power meters in ski poles
We have conducted a pilot study in the use of machine learning techniques on data from Skisens poles to identify which "gear" a skier is using (double poling or gears 2-4 in skating), based only on the sensor data from the ski poles.
Towards Machine Learning on data from Professional Cyclists
Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions.
