Tokenizing the Future: Summarizing Massive Contexts with Hierarchical Magic

May 3, 2025

Tokenizing the Future: Summarizing Massive Contexts with Hierarchical Magic

Picture this: you’re trying to get an AI to process a context so big it’s like reading War and Peace a thousand times over—100 million tokens, to be exact. Sounds like a computational nightmare, right? Well, buckle up, because research is cooking up some seriously clever ways to make this happen without melting your GPU. The secret sauce? Hierarchical and dynamic tokenization, inspired by how our brains keep track of life’s chaos without short-circuiting.

The Context Conundrum

Large Language Models (LLMs) are amazing at understanding text, but they hit a wall when it comes to context length—the number of tokens (think words or word chunks) they can juggle at once. Most models top out at a few thousand tokens, with champs like Gemini 1.5 Pro hitting 2 million. But 100 million? That’s a whole different beast. Why? Transformers, the backbone of LLMs, have a pesky quadratic scaling problem: the more tokens, the more compute and memory you need. For 100 million tokens, you’re looking at a memory hog that’d make a supercomputer sweat.

So, how do we make an LLM process a context the size of a small library without needing a data center? Enter hierarchical and dynamic tokenization, with a sprinkle of human-inspired memory tricks.

Mimicking the Human Brain

Humans don’t reprocess every memory every time we think. When you recall a book, you don’t replay every word—you grab the gist and zoom in on details if needed. This “rolling context” idea is key. We distill core ideas and build on them, like summarizing a convo as “they had a rough day” and adding new details as they come. Research is borrowing this trick to make LLMs handle massive contexts efficiently.

The goal: summarize chunks of text into compact representations, like turning 10,000 tokens into one “paragraph token,” and then work with those instead of the raw data. It’s like compressing a movie into a trailer but keeping the good bits accessible.

Hierarchical Tokenization: The Big Idea

Hierarchical tokenization is like organizing your messy desk into neat folders, subfolders, and labels. Instead of treating 100 million tokens as one giant pile, you break it into levels:

  • Bottom Level: Raw tokens (words, subwords).
  • Middle Levels: Summaries of sentences or paragraphs.
  • Top Level: A high-level summary of the whole shebang.

Research, like the Hierarchical Attention Transformers framework, shows how this works. Imagine splitting 10,000 tokens into 100 chunks of 100 tokens each. A transformer processes each chunk into a summary vector, capturing the key vibes. Then, another layer combines those 100 vectors into 10 higher-level summaries, and finally, one last layer squishes those into a single “paragraph token” vector. Boom—10,000 tokens down to one, with the essence intact.

Numbers tell the story: for 100 million tokens, summarizing every 10,000 into one token shrinks the sequence to 10,000. Attention complexity drops from O(100M²) to O(10K²)—a massive win. The Hierarchical Attention Networks (HANs) paper (2016) backs this up, using word- and sentence-level attention to summarize documents, while newer work like InfiniteHiP pushes contexts to 3 million tokens on a single GPU.

Dynamic Tokenization: The Adaptive Twist

Dynamic tokenization adds flair by tweaking how tokens are created based on the text. Got repetitive phrases in a long convo? Collapse them into fewer tokens. Research suggests this can cut token counts by 10-30%. It’s like summarizing “I said, I said, I said” into one snappy token without losing meaning. Techniques like SentencePiece or Token Merging (TOME) make this happen, adapting to the text’s complexity on the fly.

Putting It Together: A Practical Example

Let’s say you’re building an LLM to analyze a 100-million-token codebase. Here’s how you’d do it:

  1. Chunk It Up: Split the 100M tokens into 10,000 chunks of 10,000 tokens each.
  2. Summarize Hierarchically: For each 10,000-token chunk:
    • Break it into 100 sub-chunks of 100 tokens.
    • Use a transformer to summarize each sub-chunk into a vector.
    • Combine those 100 vectors into 10 higher-level vectors, then into one “paragraph token.”
  3. Dynamic Compression: Apply dynamic tokenization to squash repetitive code comments or boilerplate into fewer tokens.
  4. Retrieve on Demand: Use a retrieval system to pull only the relevant “paragraph tokens” for a query, like finding a specific function.

Result? You’re processing ~10,000 tokens instead of 100 million, and your GPU isn’t crying. The Hierarchical Transformers article (2023) shows this works for 65,536 tokens, and scaling up is just a matter of adding levels.

Research Nuggets

Here’s what the papers say:

  • InfiniteHiP (2025): Extends context to 3M tokens using token pruning and KV cache offloading. It’s a proof-of-concept for hierarchical summarization on steroids.
  • Natively Sparse Attention (NSA, 2025): Combines coarse token compression with fine-grained selection, perfect for hierarchical setups.
  • XLNet (2019): Its permutation-based training helps models understand chunked contexts, boosting hierarchical processing.

Challenges? Sure. Summaries might miss nuances, and designing the hierarchy takes finesse. But the trajectory is clear: hierarchical and dynamic tokenization is the future.

The Dev’s Toolkit

How I would approach this:

  • Model: Start with a transformer like BERT, modified for hierarchical attention (check HANs or Hierarchical Transformers).
  • Tokenizer: Use SentencePiece for dynamic tokenization, tweaking it for your data.
  • Training: Feed the model long sequences with precomputed summaries to teach it hierarchy.
  • Hardware: A single Nvidia A100 can handle this, per recent research, but scale to clusters for 100M tokens.

Conclusion

Hierarchical and dynamic tokenization is like giving LLMs a superpower: the ability to process contexts so big they’d make your laptop beg for mercy. By summarizing 10,000 tokens into one “paragraph token” and adapting to the text’s quirks, we’re inching closer to AI that thinks like us—grabbing the gist and diving into details only when needed.