Papers by Ryan Thomas
Ontologically Faithful Generation of Non-Player Character Dialogues (2024.emnlp-main)
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| Challenge: | a key challenge in creating NPC dialogues is that they should serve coherent narratives. |
| Approach: | They propose to use supervised and in-context learning techniques to generate trees of dialogue between video game characters that accurately reflect quest and entity specifications. |
| Outcome: | The proposed model performs well but room for improvement. |
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |
Analyzing Wrap-Up Effects through an Information-Theoretic Lens (2022.acl-short)
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| Challenge: | a lack of studies targeting naturalistic sentence-final reading behavior is likely to explain the lack of data on reading time (RT) data is omitted due to the confounding factors introduced by so-called "wrap-up effects" |
| Approach: | They propose to look for a link between “wrap-up effects” and information theoretic quantities such as word and context information content. |
| Outcome: | The proposed model omits data on words at the end of sentences or clauses to control for the confounding factors introduced by wrap-up effects. |