Nov 14, 2024
Large Language Models (LLMs) are everywhere these days, from autocomplete and chatbots to code generation and research tools. But how do these models actually work, and why is building them so complicated? And, more importantly, how might the new Titan approach from Google shake things up?
Let’s take a relaxed but thorough walk through the fundamentals of LLMs and see where “Titan” could fit in.
1. The Key Components of LLMs
Most modern LLMs, including GPT-style models, revolve around five main ideas:
Architecture – Typically, these are Transformer-based networks. A Transformer can handle context across sequences of tokens using “attention,” which captures relationships between tokens in a text.
Loss Function / Training Algorithm – LLMs are usually trained via next-token prediction. It’s essentially a classification problem: given the previous tokens, pick the next one. The loss function (cross-entropy) encourages the model to put high probability on the correct next token.
Data – The pretraining data is massive: from crawling billions of web pages, to carefully curated sources, to domain-specific corpora. The “secret sauce” often lies in how companies filter these data and how they might upweight “high-quality” text.
Evaluation – We used to rely on metrics like “perplexity”—how many tokens on average the model “hesitates” over. Now, we often evaluate LLMs by how well they answer open-ended questions (with humans or other models judging the quality).
Systems / Infrastructure – Training multi-billion parameter models requires huge clusters of GPUs or TPUs, plus plenty of engineering magic to keep them from bottlenecking on communication, memory, or throughput.
Why All the Fuss About Transformers?
Transformers introduced the concept of “self-attention” instead of older recurrent layers.
In practice, this meant much better parallelization on modern hardware, enabling them to scale (almost) linearly with large data sets.
The catch is that the actual attention operation can become O(n²) with increasing sequence length, which makes extremely long contexts expensive.
2. Pretraining vs. Post-Training
Model != Assistant
Pre-train step - you will get a language model - GPT-4o, o1, Sonnet 3.5, Llama 3.1, etc. Post-train step - you will get a model that can be used as an assistant - ChatGPT, Claude, Gemini, etc.
In general, LLM development splits into two major phases:
(A) Pretraining
Pretraining aims to give the model broad knowledge of how language works.
It’s a massive slog through trillions of tokens—essentially the “raw Internet.”
The main job here is to turn the model into a giant next-word predictor across varied data, from books and articles to code.
Important: models can`t produce new tokens. they only can predict next token and recombine them. So if token was not in vocab, model can`t predict it.
(B) Post-Training (Alignment to Real Use Cases)
After pretraining, the model is narrowed down to specific goals:
Supervised Fine-Tuning (SFT): Human-generated examples of question-answer pairs are used to train (fine-tune) the model to produce direct, helpful responses rather than simply continuing random Internet text.
RLHF & Alternatives (e.g., PPO, DPO): Instead of exactly matching human-written answers, the model is optimized to please humans by taking two (or more) candidate outputs and favoring whichever the human (or a reward model) likes better. This approach tries to align the model with user preferences rather than just duplicating training examples.
This second phase is what turned the generic GPT-3 model into ChatGPT, or other general-purpose AI assistants.
3. Data Churn: Why Quality Matters
A big focus today is on how well we curate and filter data:
Deduplication: Remove repeated content (like forum footers appearing over and over).
Filtering Out “Garbage,” from harmful or shock content to random HTML noise.
Domain Upsampling: Sometimes we want more math or coding data to sharpen the model’s ability in reasoning - what a suprise. code and math improve reasoning.
The more carefully curated data we have, the more “aligned” the model becomes. O pen source efforts also show how synthetic data—generated by existing strong models—can further expand training sets cheaply (though with certain risks of propagating mistakes or biases).
4. Managing Long Context
Typical Transformers have a limit to how many tokens they attend to at once—sometimes a few thousand tokens. Long or streaming tasks become challenging because of that O(n²) cost of self-attention. This is exactly where new approaches—like Google’s “Titan”—promise a breakthrough.
5. Enter Titan: A Potential Next Generation for LLMs
Google has introduced a concept they call Titan (as described in recently surfaced drafts), which attempts to address the issues of memory and extremely large contexts—possibly extending well beyond the standard thousands or so tokens.
How Titan Differs
Multiple Memory Systems
On-the-Fly Learning
Efficiency with Massive Context
Why This Might Upend Transformers
Extended Context: No more artificial cutoffs like 4k or 32k tokens. Potentially millions of tokens could be processed effectively, making Titan better for time-series, real-time streaming data, or huge code files.
Better Recall: A specialized memory block may let the model juggle way more “past” data. If that works well in practice, tasks like retrieving a detail from 50,000 tokens ago might become easier.
Of course, we still have questions: can it truly deliver the same model quality as giant Transformers do at short or medium contexts? Will this memory approach be stable in real-world, messy text? We’ll have to wait and see.
6. Systems Engineering to the Rescue
No matter how cool the model design, you still need monstrous hardware to train (and serve) these huge LLMs. Titan suggests new ways to keep training efficient:
Memory Updates in Parallel: The new memory modules are designed so we can update them without blocking the entire pipeline, presumably benefiting from GPU/TPU or specialized hardware concurrency.
Mixed Precision & Operator Fusion: Standard techniques to ensure LLM training doesn’t cripple GPU throughput.
Filtering & Curated Data: Even if Titan changes how memory is represented, data is still the lifeblood of any language model.
Putting it all together, Titan might be Google’s experiment to see if we can keep scaling up context length without incurring exponential training costs.
7. So, What’s Next?
We’re in a stage where it feels like the entire world of LLMs revolves around Transformers… but as soon as someone cracks the code on extremely long context or more dynamic forms of memory, we might see a shift. If Titan (or any other advanced memory-based approach) proves itself by handling 2M tokens gracefully, that could indeed be a “nail in the coffin” for plain Transformers—perhaps as soon as 2025.
In practice, though, one architecture rarely kills off another overnight. We may see hybrid solutions for quite some time. Still, these new ideas from Google are fascinating, especially if they combine Titan’s memory with powerful hardware optimization.
8. Conclusion
LLMs are remarkable at capturing patterns in language, but they come with heavy demands for data, compute, and careful engineering. Transformers have defined the current era, yet they stumble with extremely long inputs and dynamic updates during inference. Titan’s multi-memory approach strives to fix exactly that.
Will Titan replace Transformers entirely? It’s too soon to say. But the promise of better long-term memory, more efficient context handling, and on-the-fly learning is definitely worth watching. As these models evolve, expect bigger leaps, new breakthroughs, and—yes—lots more GPU hours in the process.