more words added

README.md

@@ -22,11 +22,13 @@ It's becoming difficult to catch up with the AI space. This doc will can be used
 |FLAN       |	Fine tuning Language models	                                                        |       https://arxiv.org/pdf/2210.11416.pdf
 |FLOPS      |	Floating Point Operations Per Second	                                            |       https://en.wikipedia.org/wiki/FLOPS
 |FLoRes     |	*F*acebook *Lo*w *Res* Machine Translation Benchmark is a low-resource MT dataset.  |       https://github.com/facebookresearch/flores
+|GSM8K      |	Grade School Math 8K, GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.	                                                        |       https://arxiv.org/pdf/2109.01152.pdf
 |HNSW       |	Hierarchical Navigable Small Worlds	                                                |       https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
 |LaMDA      |   Language Model for Dialogue Applications	                                        |       https://arxiv.org/pdf/2201.08239.pdf
 |LLaMA      |	Large Language Model Meta AI	                                                    |       https://ai.meta.com/blog/large-language-model-llama-meta-ai/
 |LLaSM      |   Large Language and Speech Model                                                     |       https://arxiv.org/pdf/2308.15930.pdf
 |LLM        |	Large Language Model: An AI model trained on mass amounts of text data to understand language and generate novel content in human-like language.	
+|MAWPS      |	A Math Word Problem Repository is an online repository of Math Word Problems, to provide a unified testbed to evaluate different algorithms. | https://github.com/sroy9/mawps
 |ML         |	Machine Learning, A component in AI that allows computers to learn and make better predictive outcomes without explicit programming. Can be coupled with training sets to generate new content.	
 |MLM        |	Masked Language Model	
 |MRC        |   Machine Reading Comprehension  
@@ -39,6 +41,7 @@ It's becoming difficult to catch up with the AI space. This doc will can be used
 |RAG        |   Retriever-Augmented Generation is an AI framework that combines an information retrieval component with a text generation model to improve the quality of responses generated by LLMs.      |       https://arxiv.org/pdf/2005.11401.pdf
 |RoBERTa    |	Robustly Optimized BERT Approach	                                                |       https://arxiv.org/pdf/1907.11692.pdf
 |SQuAD      |	Stanford Question Answering Dataset	                                                |       https://arxiv.org/pdf/1606.05250.pdf
+|SVAMP      |   Simple Variations on Arithmetic Math word Problems is a challenge set to enable more robust evaluation of automatic MWP (Math Word Problem) solvers | https://github.com/arkilpatel/SVAMP
 |T5         |	Text to Text Transfer Transformer	                                                |       https://huggingface.co/docs/transformers/model_doc/t5
 |XLM        |	Cross-lingual Language Models	                                                    |       https://arxiv.org/pdf/1901.07291.pdf
 |XLU        |	Cross-lingual Understanding