collaborators

7 papers

cs.CL2024

How Well Do Large Language Models Disambiguate Swedish Words?

Richard Johansson

We evaluate a battery of recent large language models on two benchmarks for word sense disambiguation in Swedish. At present, all current models are less accurate than the best sup…

cs.CL2024

What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?

Richard Johansson

We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happen…

cs.CL2024

Can Large Language Models (or Humans) Disentangle Text?

Nicolas Audinet de Pieuchon, Adel Daoud, Connor Thomas Jerzak +2

We investigate the potential of large language models (LLMs) to disentangle text variables--to remove the textual traces of an undesired forbidden variable in a task sometimes know…

cs.CL2023

The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models

Lovisa Hagström, Denitsa Saynova, Tobias Norlund +2

Large Language Models (LLMs) make natural interfaces to factual knowledge, but their usefulness is limited by their tendency to deliver inconsistent answers to semantically equival…

cs.CL2023

An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems

Mehrdad Farahani, Richard Johansson

Autoregressive models used to generate responses in open-domain dialogue systems often struggle to take long-term context into account and to maintain consistency over a dialogue.…

cs.CL2023

On the Generalization Ability of Retrieval-Enhanced Transformers

Tobias Norlund, Ehsan Doostmohammadi, Richard Johansson +1

Recent work on the Retrieval-Enhanced Transformer (RETRO) model has shown that off-loading memory from trainable weights to a retrieval database can significantly improve language…