In this paper, we describe our submission for the shared task on Perspective-aware Healthcare Answer Summarization. Our system consists of two quantized models of the LlaMA family, applied across fine-tuning and few-shot settings. Additionally, we adopt the SumCoT prompting technique to improve the factual correctness of the generated summaries. We show that SumCoT yields more factually accurate summaries, even though this improvement comes at the expense of lower performance on lexical overlap and semantic similarity metrics such as ROUGE and BERTScore. Our work highlights an important trade-off when evaluating summarization models.

In this paper, we describe our submission for the NLI4CT 2024 shared task on robust Natural Language Inference over clinical trial reports. Our system is an ensemble of nine diverse models which we aggregate via majority voting. The models use a large spectrum of different approaches ranging from a straightforward Convolutional Neural Network over fine-tuned Large Language Models to few-shot-prompted language models using chain-of-thought reasoning.Surprisingly, we find that some individual ensemble members are not only more accurate than the final ensemble model but also more robust.