| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
| Approach: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
| Outcome: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
Similar Papers
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
Copied to clipboard
Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
| Outcome: | The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods. |
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
| Outcome: | The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses. |
Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | General-purpose Language Models have changed the world of Natural Language Processing, if not the world itself. |
| Approach: | This tutorial will lay the foundations and explain the basics of evaluation and compare traditional methods to newly developed methods. |
| Outcome: | The tutorial assumes little familiarity with metrics, datasets, prompts and benchmarks . it will compare traditional methods to newly developed methods . |
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts (2021.acl-tutorials)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations (2021.acl-demo)
Copied to clipboard
| Challenge: | ACL-IJCNLP 2021 will be an online conference . submissions range from early prototypes to mature production-ready systems . |
| Approach: | the ACL-IJCNLP 2021 will be an online conference . the system demonstration track invites submissions ranging from early prototypes to mature production-ready systems. |
| Outcome: | the ACL-IJCNLP 2021 system demonstration track received 133 submissions . the submission rate was 32.3% . |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
Copied to clipboard
| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research (2021.acl-short)
Copied to clipboard
| Challenge: | a language benchmark is a task devised that is restricted enough to be managable with current methods, but is deemed challenging enough to serve as a benchmark. |
| Approach: | They propose to use a language task as a benchmark and a baseline model to argue it is challenging enough to be a good one. |
| Outcome: | The proposed language benchmarks are based on a dataset and a language task . the proposed benchmarks can be used to measure progress towards the goal of the research . |
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Tutorial Abstracts (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials (2021.naacl-tutorials)
Copied to clipboard
| Challenge: | NAACL 2021 tutorials session is a conference for researchers to present on a topic of importance . a total of 35 tutorial submissions were received, of which 6 were selected for presentation . |
| Approach: | NAACL 2021 tutorials session is organized to give conference attendees a comprehensive introduction from expert researchers to a topic of importance drawn from our research field. |
| Outcome: | the tutorials committee selected 6 tutorials for presentation at NAACL 2021 . the topics chosen this year range from transformers to crowdsourcing . |
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are now available online. |
| Approach: | EMNLP 2025 conference on empirical methods in natural language processing held in Suzhou, china, on November 4-9, 2025. 77 papers accepted for inclusion in proceedings, resulting in 38% acceptance rate. |
| Outcome: | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing are published . the conference accepted 77 papers, with a 38% acceptance rate . |