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DTSTAMP:20260513T161330Z
SUMMARY:AI Lund lunch seminar: Stop wasting energy! Performance vs Power co
 nsumption in classification
DESCRIPTION:Kontakt: Jonas.Wisbrant@control.lth.se\n\nA recording of the se
 minar is available ai.lu.se.Topic:&nbsp\;Stop wasting energy! Performance 
 vs Power consumption in classificationWhen: 27 August at 12.00-13.00Where:
  OnlineSpeaker: Andreas Bexell\, Industrial PhD student\, Software Develop
 ment and Environments\, Lund University and EricssonSpoken language: Engli
 shAbstractThe use of large language models is sweeping into many fields an
 d is being explored for many natural language tasks\, ranging from text ge
 neration to classification. The introduction of tools like ChatGPT and co-
 pilot has been transformative for software development\, engineering\, and
  education. Many efforts aim to employ natural language processing and AI 
 for different applications and domains throughout the public and private s
 ectors.Large language models provide impressive results but require more t
 ime and energy than other models. For some tasks\, simpler models may be m
 ore sustainable.In an experiment\, we construct natural language classifie
 rs of different complexity for a use case in the software engineering doma
 in and demonstrate results similar to the state-of-the-art.We find that si
 mpler models perform similarly to LLMs while using a fraction of the energ
 y. We also find that contrary to expectations\, the classifiers based on l
 arge models do not generalize better to previously unseen data. Based on o
 ur results\, we recommend careful model selection and that models for prac
 tical applications should be specialized using training data from the targ
 et set. Choosing a small model can lead to a significant reduction in powe
 r usage without compromising performance.&nbsp\;\n\nMer information om hä
 ndelsen: https://www.medarbetarwebben.lu.se/evenemang/ai-lund-lunch-semina
 r-stop-wasting-energy-performance-vs-power-consumption-classification
DTSTART;TZID=GMT:20250827T100000
DTEND;TZID=GMT:20250827T110000
LOCATION:Online - link by registration
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