add: week

2022Fall_AntNLP/README.md

@@ -44,7 +44,7 @@ Week   	| Date 	| Speaker   | Paper   	| Materials
 3 	|10.5 	| 国庆节   |-|-
 4 	|10.12 	| 王志承 	||
 5 	|10.19 	| 杜威 	|Document-Level Event Extraction|[Slides](https://github.com/AntNLP/seminar/blob/master/2022Fall_AntNLP/week5/2022-10-24%E7%BB%84%E4%BC%9A.pdf)
-6 	|10.26	| 刘宇芳  	||
+6 	|10.26	| 刘宇芳  	|Calibrating Factual Knowledge in PLMs|[Slides](https://github.com/AntNLP/seminar/blob/master/2022Fall_AntNLP/week6/1102.pdf)
 7 	|11.2	| 杨晰 	|| 
 8 	|11.9	| 汪杰	|| 
 9 	|11.16	| 李雨倩 	||

2022Fall_PLM/README.md

@@ -20,7 +20,7 @@ Welcome to AntNLP Seminar for PLMs 2022 Fall. : )
 Week | Date  | Speaker | Topic     |Paper      | Key Phrase |Slides
 ---- | ----  | ----	   | ---- 	   | ----      | ----      | ---- 
 1 	 | 10.20  | 李雨倩   | Preliminaries: Past, Architectures, Pre-training, Capabilities | 1. [Attention Is All You Need](https://arxiv.org/abs/1706.03762) (Transformer) <br>   2.  [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/pdf/1810.04805.pdf) (Bert) <br> 3. [ Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) (OpenAI GPT) <br> 4. [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf) (RoBERTa) <br> 5. [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/pdf/2003.10555.pdf) (ELECTRA)|   transformer; encoder-only models|[Slides](https://github.com/AntNLP/seminar/tree/master/2022Fall_PLM/week1/ppt.pptx)
-2 	 | 10.27  | 刘燕婷	| Other Pretraining Language Models I|1. [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) (T5)<br> 2. [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) (BART) <br> 3. [ mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/pdf/2010.11934.pdf) (mT5) <br> 4. [AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2seq Model](https://arxiv.org/pdf/2208.01448.pdf) (AlexaTM) | encoder-decoder models |
+2 	 | 10.27  | 刘燕婷	| Other Pretraining Language Models I|1. [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) (T5)<br> 2. [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) (BART) <br> 3. [ mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/pdf/2010.11934.pdf) (mT5) <br> 4. [AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2seq Model](https://arxiv.org/pdf/2208.01448.pdf) (AlexaTM) | encoder-decoder models |[Slides](https://github.com/AntNLP/seminar/tree/master/2022Fall_PLM/week2/ppt.pptx)
 3 	 | 11.3  |  杜威    | Other Pretraining Language Models II          | 1. [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165.pdf)(GPT3) <br> 2. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT2) <br> 3. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/pdf/2204.02311.pdf) (PaLM) <br> 4. [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/pdf/2205.01068.pdf) (OPT)        |   decoder-only models        |
 4 	 | 11.10 | 丁炫文   |  Prompting for few-shot learning |1. [ Making Pre-trained Language Models Better Few-shot Learners](https://arxiv.org/pdf/2012.15723.pdf) <br> 2. [ How Many Data Points is a Prompt Worth?](https://arxiv.org/pdf/2103.08493.pdf) <br> 3. [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://arxiv.org/pdf/2001.07676.pdf) <br> 4. [True Few-Shot Learning with Language Models](https://arxiv.org/pdf/2105.11447.pdf) <br> 5. [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://arxiv.org/pdf/2106.13353.pdf) |  PET         |    
 5 	 | 11.17 | 汪杰 | In-context learning and limits         | 1.  [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) <br> 2. [On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model](https://arxiv.org/abs/2204.13509) <br> 3. [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/pdf/2202.12837.pdf)<br> 4. [Impact of Pretraining Term Frequencies on Few-Shot Reasoning](https://arxiv.org/pdf/2202.07206.pdf) <br> 5. [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://arxiv.org/abs/2109.01247) |  In-context learning         |