In natural language processing (NLP) field, it is hard to augmenting text due to high complexity of language. Not every word we can replace it by others such as a, an, the. Also, not every word has synonym. Even changing a word, the context will be totally difference. On the other hand, generating augmented image in computer vision area is relative easier. Even introducing noise or cropping out portion of image, model can still classify the image.
Recent explosion in the popularity of large language models like ChatGPT has led to their increased usage in classical NLP tasks like language classification. This involves providing a context…
Large language models (LLMs) have proven to be valuable tools, but they often lack reliability. Many instances have surfaced where LLM-generated responses included false information. Specifically…
TLDR — Extractive question answering is an important task for providing a good user experience in many applications. The popular Retriever-Reader framework for QA using BERT can be difficult to scale…
B. Pang, and L. Lee. Proceedings of the Association for Computational Linguistics (ACL), page 271--278. Association for Computational Linguistics, (2004)
S. Basu, A. Banerjee, and R. Mooney. Proceedings of the 2004 SIAM International Conference on Data Mining, page 333--344. Lake Buena Vista, FL, Society for Industrial and Applied Mathematics, (April 2004)
N. Hossain, J. Krumm, and M. Gamon. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), page 133--142. (2019)