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SUMMARY:Analysing Memorisation in Classification and Translation through
  Localisation and Cartography - Verna Dankers (University of Edinburgh) 
DTSTART:20250124T120000Z
DTEND:20250124T130000Z
UID:TALK226609@talks.cam.ac.uk
CONTACT:Suchir Salhan
DESCRIPTION:Memorisation is a natural part of learning from real-world dat
 a: neural models pick up on atypical input-output combinations and store t
 hose training examples in their parameter space. That this happens is well
 -known\, but which examples require memorisation and where in the millions
  (or billions) of parameters memorisation occurs are questions that remain
  largely unanswered.\nIn this talk\, I first elaborate on the localisation
  question by examining memorisation in the context of classification in fi
 ne-tuned PLMs\, using 12 tasks. Our findings give nuance to the generalisa
 tion-first memorisation-second hypothesis dominant in the literature and f
 ind memorisation to be a gradual process rather than a localised one.\n\nS
 econdly\, I discuss memorisation from the viewpoint of the data using neur
 al machine translation (NMT) models by putting individual data points on a
  memorisation-generalisation map.\n\nI illustrate how the data points' cha
 racteristics are predictive of memorisation in NMT and describe the influe
 nce that subsets of that map have on NMT systems' performance.\n\nThe talk
  is based on the following two publications:\n\nDankers\, V.\, & Titov\, I
 . (2024). Generalisation First\, Memorisation Second? Memorisation Localis
 ation for Natural Language Classification Tasks. ACL-Findings\n\nDankers\,
  V.\, Titov\, I.\, & Hupkes\, D. (2023). Memorisation Cartography: Mapping
  out the Memorisation-Generalisation Continuum in Neural Machine Translati
 on. EMNLP
LOCATION:Room SS03 with Hybrid Format. Here is the Zoom link for those tha
 t wish to join online: https://cam-ac-uk.zoom.us/j/4751389294?pwd=Z2ZOSDk0
 eG1wZldVWG1GVVhrTzFIZz09
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