As large language models continue to develop in the field of AI, text generation systems are
susceptible to a worrisome phenomenon known as hallucination. In this study, we …

Large language models (LLMs) have become the state of the art in natural language
processing. The massive adoption of generative LLMs and the capabilities they have shown …

While automatic dialogue tutors hold great potential in making education personalized and
more accessible, research on such systems has been hampered by a lack of sufficiently …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

Hallucinations pose a significant challenge to the reliability of neural models for abstractive
summarisation. While automatically generated summaries may be fluent, they often lack …

Recently, the development of open-source large language models (LLMs) has advanced
rapidly. Nevertheless, due to data constraints, the capabilities of most open-source LLMs are …

Advancements in LLMs have recently unveiled challenges tied to computational efficiency
and continual scalability due to their requirements of huge parameters, making the …

Factual faithfulness is a crucial requirement in information-seeking dialogue: the system
should respond to the user queries so that the responses are meaningful and aligned with …

Models trained on different datasets can be merged by a weighted-averaging of their
parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of …

This paper introduces ClimateGPT, a model family of domain-specific large language
models that synthesize interdisciplinary research on climate change. We trained two 7B …