Search Results - "Tar, Chris"

Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation by Vu, Tu, Krishna, Kalpesh, Alzubi, Salaheddin, Tar, Chris, Faruqui, Manaal, Sung, Yun-Hsuan

“…As large language models (LLMs) advance, it becomes more challenging to reliably evaluate their output due to the high costs of human evaluation. To make…”
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FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation by Vu, Tu, Iyyer, Mohit, Wang, Xuezhi, Constant, Noah, Wei, Jerry, Wei, Jason, Tar, Chris, Sung, Yun-Hsuan, Zhou, Denny, Le, Quoc, Luong, Thang

“…Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this…”
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PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification by Yang, Yinfei, Zhang, Yuan, Tar, Chris, Baldridge, Jason

“…Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) consists of challenging…”
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Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction by Yang, Yinfei, Agarwal, Oshin, Tar, Chris, Wallace, Byron C, Nenkova, Ani

“…Modern NLP systems require high-quality annotated data. In specialized domains, expert annotations may be prohibitively expensive. An alternative is to rely on…”
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A Growing Long-term Episodic & Semantic Memory by Pickett, Marc, Al-Rfou, Rami, Shao, Louis, Tar, Chris

“…The long-term memory of most connectionist systems lies entirely in the weights of the system. Since the number of weights is typically fixed, this bounds the…”
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Multilingual Universal Sentence Encoder for Semantic Retrieval by Yang, Yinfei, Cer, Daniel, Ahmad, Amin, Guo, Mandy, Law, Jax, Constant, Noah, Abrego, Gustavo Hernandez, Yuan, Steve, Tar, Chris, Sung, Yun-Hsuan, Strope, Brian, Kurzweil, Ray

“…We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures. The…”
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Universal Sentence Encoder by Cer, Daniel, Yang, Yinfei, Kong, Sheng-yi, Hua, Nan, Limtiaco, Nicole, John, Rhomni St, Constant, Noah, Guajardo-Cespedes, Mario, Yuan, Steve, Tar, Chris, Sung, Yun-Hsuan, Strope, Brian, Kurzweil, Ray

“…We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and…”
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