new literature

amu-literature.bib

@@ -1484,6 +1484,72 @@
   url     = {https://api.semanticscholar.org/CorpusID:276725396},
 }
 
+@Article{liu2024ai,
+  author  = {Liu, Xurong and Kong, Leilei},
+  journal = {Working notes of clef},
+  title   = {AI text detection method based on perplexity features with strided sliding window},
+  year    = {2024},
+  file    = {:paper-261.pdf:PDF},
+  groups  = {evaluation},
+}
+
+@InProceedings{wu-etal-2024-perplexing,
+  author    = {Wu, Yaru and Bizzoni, Yuri and Moreira, Pascale and Nielbo, Kristoffer},
+  booktitle = {Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)},
+  title     = {Perplexing Canon: A study on {GPT}-based perplexity of canonical and non-canonical literary works},
+  year      = {2024},
+  address   = {St. Julians, Malta},
+  editor    = {Bizzoni, Yuri and Degaetano-Ortlieb, Stefania and Kazantseva, Anna and Szpakowicz, Stan},
+  month     = mar,
+  pages     = {172--184},
+  publisher = {Association for Computational Linguistics},
+  abstract  = {This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect {\textquotedblleft}strategies{\textquotedblright} to achieve an informationally dense literary style.},
+  file      = {:2024.latechclfl-1.16.pdf:PDF},
+  groups    = {evaluation, read-asap, comparison-literary-texts},
+  ranking   = {rank5},
+  url       = {https://aclanthology.org/2024.latechclfl-1.16/},
+}
+
+@InProceedings{jacobsen2024patterns,
+  author       = {Jacobsen, Mia and Bizzoni, Yuri and Moreira, Pascale Feldkamp and Nielbo, Kristoffer L},
+  booktitle    = {Proceedings of the Computational Humanities Research Conference 2024},
+  title        = {Patterns of Quality: Comparing Reader Reception Across Fanfiction and Commercially Published Literature},
+  year         = {2024},
+  organization = {in CEUR Workshop Proceedings},
+  pages        = {718--739},
+  volume       = {3834},
+  file         = {:paper106.pdf:PDF},
+  groups       = {comparison-literary-texts},
+}
+
+@Article{bizzoni2024good,
+  author  = {Bizzoni, Yuri and Feldkamp, Pascale and Lassen, Ida Marie and Jacobsen, Mia and Thomsen, Mads Rosendahl and Nielbo, Kristoffer},
+  journal = {arXiv preprint arXiv:2404.04022},
+  title   = {Good books are complex matters: Gauging complexity profiles across diverse categories of perceived literary quality},
+  year    = {2024},
+  file    = {:bizzoni2024good - Good Books Are Complex Matters_ Gauging Complexity Profiles across Diverse Categories of Perceived Literary Quality.pdf:PDF:http\://arxiv.org/pdf/2404.04022v2},
+  groups  = {comparison-literary-texts},
+}
+
+@InProceedings{bizzoni2024matter,
+  author    = {Bizzoni, Yuri and Moreira, Pascale Feldkamp and Lassen, Ida Marie S and Thomsen, Mads Rosendahl and Nielbo, Kristoffer},
+  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
+  title     = {A matter of perspective: Building a multi-perspective annotated dataset for the study of literary quality},
+  year      = {2024},
+  pages     = {789--800},
+  file      = {:2024.lrec-main.71.pdf:PDF},
+  groups    = {comparison-literary-texts},
+}
+
+@Article{fang2024wrong,
+  author  = {Fang, Lizhe and Wang, Yifei and Liu, Zhaoyang and Zhang, Chenheng and Jegelka, Stefanie and Gao, Jinyang and Ding, Bolin and Wang, Yisen},
+  journal = {arXiv preprint arXiv:2410.23771},
+  title   = {What is Wrong with Perplexity for Long-context Language Modeling?},
+  year    = {2024},
+  file    = {:2410.23771v3.pdf:PDF},
+  groups  = {evaluation},
+}
+
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