Papers with text prediction
Gatekeeper to save COGS and improve efficiency of Text Prediction (2023.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) require massive amounts of computation and storage, such an approach incurs network and high execution cost. |
| Approach: | They propose a model gatekeeper to stop LLM calls that result in incorrect predictions . they show it can save 46.6% of COGS and improve user experience by not showing incorrect predictions. |
| Outcome: | The proposed model gatekeeper saves 46.6% of COGS and improves user experience . it also improves the suggestion rate of the proposed model by 73% . |
Performance and Risk Trade-offs for Multi-word Text Prediction at Scale (2023.findings-eacl)
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Aniket Vashishtha, S Sai Prasad, Payal Bajaj, Vishrav Chaudhary, Kate Cook, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury
| Challenge: | Large Language Models (LLMs) generate ethically inappropriate texts even for seemingly innocuous contexts. |
| Approach: | They propose to use large language models to detect and filter toxic content in text prediction tasks by evaluating their toxicity detection approaches against a manually crafted CheckList of harms. |
| Outcome: | The proposed methods are compared against a checklist of harms targeted at different groups and different levels of severity in English. |
Indigenous language technologies in Canada: Assessment, challenges, and successes (C18-1)
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Patrick Littell, Anna Kazantseva, Roland Kuhn, Aidan Pine, Antti Arppe, Christopher Cox, Marie-Odile Junker
| Challenge: | There are approximately 60 Indigenous languages currently spoken in Canada. |
| Approach: | They examine which technologies have been developed and which are feasible to develop for the 60 Indigenous languages spoken in Canada. |
| Outcome: | The proposed technologies are based on the existing technologies and are feasible for most or all of these languages. |
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software (2020.coling-main)
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Roland Kuhn, Fineen Davis, Alain Désilets, Eric Joanis, Anna Kazantseva, Rebecca Knowles, Patrick Littell, Delaney Lothian, Aidan Pine, Caroline Running Wolf, Eddie Santos, Darlene Stewart, Gilles Boulianne, Vishwa Gupta, Brian Maracle Owennatékha, Akwiratékha’ Martin, Christopher Cox, Marie-Odile Junker, Olivia Sammons, Delasie Torkornoo, Nathan Thanyehténhas Brinklow, Sara Child, Benoît Farley, David Huggins-Daines, Daisy Rosenblum, Heather Souter
| Challenge: | This paper describes the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages and extending their use. |
| Approach: | They describe the first phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages. |
| Outcome: | The proposed software will help Indigenous communities preserve and revitalize their languages and extend their use. |