58 lines
4.7 KiB
Markdown
58 lines
4.7 KiB
Markdown
# Research Material
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## BPE
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- [BPE Wikipedia](https://en.wikipedia.org/wiki/Byte-pair_encoding)
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- [BPE Hugging Face](https://huggingface.co/learn/llm-course/chapter6/5)
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- [BPE GeeksForGeeks](https://www.geeksforgeeks.org/nlp/byte-pair-encoding-bpe-in-nlp/)
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- [BPE Medium Chetna Khanna](https://medium.com/data-science/byte-pair-encoding-subword-based-tokenization-algorithm-77828a70bee0)
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- [Stack Overflow "Explain bpe (Byte Pair Encoding) with examples?"](https://stackoverflow.com/questions/50583254/explain-bpe-byte-pair-encoding-with-examples)
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- [Implementing a byte pair encoding(BPE) Tokenizer from scratch](https://sebastianraschka.com/blog/2025/bpe-from-scratch.html)
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- [Thoretical Analysis of Byte-Pair Encoding](https://arxiv.org/pdf/2411.08671)
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- [A Formal Perspective on Byte-Pair Encoding](https://aclanthology.org/2023.findings-acl.38v2.pdf)
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- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://arxiv.org/pdf/2004.03720)
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- [Byte pair encoding: a text compression scheme that accelerates pattern matching](https://www.researchgate.net/profile/Takeshi-Shinohara/publication/2310624_Byte_Pair_Encoding_A_Text_Compression_Scheme_That_Accelerates_Pattern_Matching/links/02e7e522f8ea00c318000000/Byte-Pair-Encoding-A-Text-Compression-Scheme-That-Accelerates-Pattern-Matching.pdf)
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- [A Formal Perspective on Byte-Pair Encoding](https://arxiv.org/pdf/2306.16837)
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- [Controlling byte pair encoding for neural machine translation](https://ieeexplore.ieee.org/abstract/document/8300571)
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- [Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removal](https://ojs.aaai.org/index.php/AAAI/article/view/34633)
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- [Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization](https://arxiv.org/pdf/2508.04796)
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- [Code Completion using Neural Aention and Byte Pair Encoding](https://arxiv.org/pdf/2004.06343)
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- [Getting the most out of your tokenizer for pre-training and domain adaptation](https://arxiv.org/html/2402.01035v2)
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## Embedder
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- [ROFORMER: ENHANCED TRANSFORMER WITH ROTARY POSITION EMBEDDING](https://arxiv.org/pdf/2104.09864)
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- [You could have designed state of the art positional encoding](https://huggingface.co/blog/designing-positional-encoding)
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- [Rotary Embeddings: A Relative Revolution](https://blog.eleuther.ai/rotary-embeddings/)
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- [Round and Round We Go! What makes Rotary Positional Encodings useful?](https://arxiv.org/html/2410.06205v1)
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- [Inside RoPE: Rotary Magic into Position Embeddings](https://learnopencv.com/rope-position-embeddings/)
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- [What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information](https://openreview.net/pdf?id=e5Mv7iWfVW)
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- [A gentle introduction to Rotary Position Embedding](https://krasserm.github.io/2022/12/13/rotary-position-embedding/)
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- [Context-aware Rotary Position Embedding](https://arxiv.org/pdf/2507.23083)
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- [LIERE: GENERALIZING ROTARY POSITION ENCODINGS TO HIGHER DIMENSIONAL INPUTS](https://openreview.net/pdf?id=xHMMt7r3GW)
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- [Rotary Positional Embeddings (RoPE)](https://nn.labml.ai/transformers/rope/index.html)
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- [Decoding Llama3: An explainer for tinkerers](https://hasgeek.com/simrathanspal/the-llama3-guide/sub/decoding-llama3-part-4-rotary-positional-embedding-3K8ZHpdLi6E56N8ejnaWzm)
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## Attention
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- [Standard Self-Attention (Attention is all you need)](https://arxiv.org/pdf/1706.03762)
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- [TransMLA: Multi-Head Latent Attention Is All You Need](https://arxiv.org/pdf/2502.07864)
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- [A Gentle Introduction to Multi-Head Latent Attention (MLA)](https://machinelearningmastery.com/a-gentle-introduction-to-multi-head-latent-attention-mla/)
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- [Understanding Multi-Head Latent Attention](https://planetbanatt.net/articles/mla.html)
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- [DeepSeek's Multi-Head Latent Attention](https://liorsinai.github.io/machine-learning/2025/02/22/mla.html)
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- [MatchFormer: Interleaving Attention in Transformers for Feature Matching](https://arxiv.org/pdf/2203.09645)
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- [FIT: Far-reaching Interleaved Transformers](https://arxiv.org/pdf/2305.12689)
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- [Gemma explained: What’s new in Gemma 3](https://developers.googleblog.com/en/gemma-explained-whats-new-in-gemma-3/)
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- [The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)
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- [Attention was never enough: Tracing the rise of hybrid LLMs](https://www.ai21.com/blog/rise-of-hybrid-llms/)
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## Spanned Masking
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- [Salient Span Masking for Temporal Understanding](https://arxiv.org/pdf/2303.12860)
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- [PMI-MASKING: PRINCIPLED MASKING OF CORRELATED SPANS](https://arxiv.org/pdf/2010.01825)
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## Models
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- [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/pdf/2204.05832)
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