nlp16 DPO: Training A Language Model To Satisfy Human Preferences Direct Preference Optimization:Your Language Model is Secretly a Reward ModelRafael Rafailov∗† Archit Sharma∗† Eric Mitchell∗† Stefano Ermon†‡ Christopher D. Manning† Chelsea Finn†13 Dec 2023 Policy Preferred by HumansLarge-scale unsupervised language models are known to solve various tasks based on extensive knowledges. These generative models produce responses based on their policy. RLHF (Rei.. 2024. 7. 4. 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. 이전 1 2 3 4 다음
