1 code implementation • EMNLP (ACL) 2021 • Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models.
no code implementations • 3 Feb 2024 • Gourab Dey, Adithya V Ganesan, Yash Kumar Lal, Manal Shah, Shreyashee Sinha, Matthew Matero, Salvatore Giorgi, Vivek Kulkarni, H. Andrew Schwartz
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of training data.
no code implementations • 16 Nov 2023 • Yash Kumar Lal, Li Zhang, Faeze Brahman, Bodhisattwa Prasad Majumder, Peter Clark, Niket Tandon
Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need.
no code implementations • 29 Jun 2023 • Dhruv Verma, Yash Kumar Lal, Shreyashee Sinha, Benjamin Van Durme, Adam Poliak
We present PaRTE, a collection of 1, 126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing.
no code implementations • 1 Jun 2023 • Adithya V Ganesan, Yash Kumar Lal, August Håkan Nilsson, H. Andrew Schwartz
Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting.
1 code implementation • Findings (ACL) 2021 • Yash Kumar Lal, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
They are especially worse on questions whose answers are external to the narrative, thus providing a challenge for future QA and narrative understanding research.
1 code implementation • ACL 2021 • Qingqing Cao, Yash Kumar Lal, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian
We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, Aaron Steven White
We introduce five new natural language inference (NLI) datasets focused on temporal reasoning.
no code implementations • WS 2019 • Kelly Marchisio, Yash Kumar Lal, Philipp Koehn
We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019).
no code implementations • ACL 2019 • Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, Philipp Koehn
The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words.
no code implementations • 2 Aug 2018 • Vaibhav Kumar, Mrinal Dhar, Dhruv Khattar, Yash Kumar Lal, Abhimanshu Mishra, Manish Shrivastava, Vasudeva Varma
We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture.
no code implementations • 4 Oct 2017 • Vaibhav Kumar, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, Vasudeva Varma
The application of neural networks for this task has only been explored partially.