Papers with text composition
Speakerly: A Voice-based Writing Assistant for Text Composition (2023.emnlp-industry)
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Dhruv Kumar, Vipul Raheja, Alice Kaiser-Schatzlein, Robyn Perry, Apurva Joshi, Justin Hugues-Nuger, Samuel Lou, Navid Chowdhury
| Challenge: | Speakerly TM is a voice-based writing assistance system that works across the different stages of writing. |
| Approach: | They propose a voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes. |
| Outcome: | The proposed system can be used for email, instant messages, and notes. |
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. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |