Prompt-based usage of Large Language Models (LLMs) is an increasingly popular way to tackle many well-known natural language problems. This trend is due, in part, to the appeal of the In-Context Learning (ICL) prompt set-up, in which a few selected training examples are provided along with the inference request. ICL, a type of few-shot learning, is especially attractive for natural language processing (NLP) tasks defined for specialised domains, such as entity extraction from scientific documents, where the annotation is very costly due to expertise requirements for the annotators. In this paper, we present a comprehensive analysis of in-context sample selection methods for entity extraction from scientific documents using GPT-3.5 and compare these results against a fully supervised transformer-based baseline. Our results indicate that the effectiveness of the in-context sample selection methods is heavily domain-dependent, but the improvements are more notable for problems with a larger number of entity types. More in-depth analysis shows that ICL is more effective for low-resource set-ups of scientific information extraction

Lay summarisation aims at generating a summary for non-expert audience which allows them to keep updated with latest research in a specific field. Despite the significant advancements made in the field of text summarisation, lay summarisation remains relatively under-explored. We present a comprehensive set of experiments and analysis to investigate the effectiveness of existing pre-trained language models in generating lay summaries. When evaluate our models using a BioNLP Shared Task, BioLaySumm, our submission ranked second for the relevance criteria and third overall among 21 competing teams.

Transformer encoder models exhibit strong performance in single-domain applications. However, in a cross-domain situation, using a sub-word vocabulary model results in sub-word overlap. This is an issue when there is an overlap between sub-words that share no semantic similarity between domains. We hypothesize that alleviating this overlap allows for a more effective modeling of multi-domain tasks; we consider the biomedical and general domains in this paper. We present a study on reducing sub-word overlap by scaling the vocabulary size in a Transformer encoder model while pretraining with multiple domains. We observe a significant increase in downstream performance in the general-biomedical cross-domain from a reduction in sub-word overlap.