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1. https://openai.com/blog/introducing-text-and-code-embeddings/

2. https://platform.openai.com/docs/guides/embeddings/use-cases

3. https://x.com/arvind_io/status/1488257004783112192

4. https://www.askviable.com/blog/why-we-chose-gpt-3-embeddings-for-the-clustering-behind-our-feedback-reports

5. Contrastive Representation Learning: A Framework and Review

6. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

7. Natural Questions: A Benchmark for Question Answering Research

8. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

9. GPT-3: Language Models are Few-Shot Learners

10. Evaluating Large Language Models Trained on Code

11. SentEval: An Evaluation Toolkit for Universal Sentence Representations

12. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

13. SimCSE: Simple Contrastive Learning of Sentence Embeddings

14. CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

15. GraphCodeBERT: Pre-training Code Representations with Data Flow

16. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

17. BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

18. Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning

19. ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

20. SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval

21. MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

22. Image GPT (iGPT): We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples

23. Muppet: Massive Multi-task Representations with Pre-Finetuning

24. Can Pre-trained Language Models be Used to Resolve Textual and Semantic Merge Conflicts?

25. Wikipedia Bibliography:

    1. https://en.wikipedia.org/wiki/Semantic_search  :

       https://en.wikipedia.org/wiki/Semantic_search

    2. https://en.wikipedia.org/wiki/String_metric  :

       https://en.wikipedia.org/wiki/String_metric

    3. https://en.wikipedia.org/wiki/Docstring  :

       https://en.wikipedia.org/wiki/Docstring

    4. Word embedding