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SUMMARY:Causal Inference for Creativity in NLP
DTSTART:20221028T163000
DTEND:20221028T183000
DTSTAMP:20260407T111443Z
UID:8e0dac15ea605a5d8061f4aae9e413707422433be65b7d9c818b0452
CATEGORIES:Conferences - Seminars
DESCRIPTION:Molly Petersen\nEDIC candidacy exam\nExam president: Prof. Rob
 ert West\nThesis advisor: Prof. Antoine Bosselut\nThesis co-advisor: Prof.
  Lonneke van der Plas\nCo-examiner: Prof. Maria Brbic\n\nAbstract\nCreativ
 ity is important in accomplishing a wide variety of tasks\, not excluding 
 scientific discovery and problem solving. In science\, creativity still mu
 st fit into the constraints with how humans have decided to go about scien
 tific reasoning\, specifically the concepts that fit under the scientific 
 method. In this candidacy\, we cover three papers that cover the topics of
  causal inference\, a commonly employed paradigm in a variety of scientifi
 c disciplines\, as well as creativity . The first paper we cover presents 
 a paradigm for creating counterfactual text representation\, to allow for 
 the identification of the effects of a particular concept exposure of inte
 rest on a downstream classification task . Next\, we will cover a paper wh
 ere the authors finetune a language model to generate counterfactual text\
 , which aside from assisting causal inference in NLP\, is arguably itself 
 a creative task . Lastly\, we will explore a paper that presents different
  analyses for evaluating novelty in generated text .\n\nBackground papers\
 nCausaLM: Causal Model Explanation Through Counterfactual Language Models\
 nhttps://arxiv.org/pdf/2005.13407.pdf\n\nHow much do language models copy 
 from their training data? Evaluating linguistic novelty in text generation
  using RAVEN\nhttps://arxiv.org/pdf/2111.09509.pdf\n\nPolyjuice: Generatin
 g Counterfactuals for Explaining\, Evaluating\, and Improving Models\nhttp
 s://arxiv.org/abs/2101.00288\n\n 
LOCATION:BC 233 https://plan.epfl.ch/?room==BC%20233
STATUS:CONFIRMED
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