Contrasting Traditional Models and LLMs : An Evaluation Based on Text Segmentation

Jegan, RobinRobinJegan0000-0002-0388-7220Henrich, AndreasAndreasHenrich0000-0002-5074-32542026-03-312026-03-312026https://fis.uni-bamberg.de/handle/uniba/114487This paper presents a discussion of the relevancy of older natural language processing approaches compared to modern large language models (LLMs), with experimental results for a specific application: the segmentation of video transcripts. An analysis was conducted, if powerful modern LLMs are necessary for tasks such as text segmentation or if traditional and more efficient models – here TextTiling – suffice. In the end, LLMs provide comparable performance to the other models, but the results produced by TextTiling are promising and suggest a discussion about a trade-off regarding efficiency, performance, energy-consumption and other factors.eng-Contrasting Traditional Models and LLMs : An Evaluation Based on Text Segmentationconferenceobjecturn:nbn:de:bvb:473-irb-114487x