nlp15 XLNet = BERT + AR, in Permutation Setting (A quick note) Cheers! Here's to myself who recently got accepted by Carnegie Mellon University; School of Computer Science, Master of Science in Intelligent Information Systems for Fall 2024. XLNet: Generalized Autoregressive Pretraining for Language UnderstandingYang et al.2 Jan 2020 Representation Learning for NLPUnsupervised representation learning is known for handling large-scale unlabeled.. 2024. 5. 5. KMMLU: A Korean Benchmark for LLMs KMMLU: Measuring Massive Multitask Language Understanding in Korean Guijin Son, Hanwool Lee, Sungdong Kim, etc. 18 Feb 2024 MMLU There exist various benchmarks for evaluating and understanding the capabilities of Large Language Models (LLMs), such as commonsense reasoning, code generation, and multi-turn conversations. Massive Multitask Language Understanding (MMLU) is one of these benchmarks, c.. 2024. 3. 29. Predicting Spans Rather Than Tokens On BERT SpanBERT: Improving Pre-training by Representing and Predicting Spans Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, etc. 18 Jan 2020 SpanBERT Coreference task is the task of finding all expressions that refer to the same entity in a text. For example, given a text as follows: "I voted for Nadar because he was most aligned with my values", she said. 'I', '.. 2023. 4. 29. Representation Learning Basic (BERT) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Google AI Language 24 May 2019 A New Language Representation Model "A good representation is one that makes a subsequent learning task easier." The paper presents BERT(Bidirectional Encoder Representations from Transformer) which is designed to deeply learn the representations from unlabeled text on both left and ri.. 2023. 4. 25. 이전 1 2 3 4 다음
