The maxim "bigger is better" has been a defining ethos in the AI industry, with the notion that scaling up an AI model amplifies its performance. The development of Large Language Models (LLMs) in recent years has experienced a trend of explosive increase in size, signified by the number of parameters – the internal variables (or “weights”) that the model adjusts through training, instrumental in determining how the model responds to inputs.
GPT-4, one of the largest models, is estimated to possess around 1.8 trillion parameters — 10x more than its predecessor, GPT-3. And this trend is set to continue its blistering pace. According to forecasts by Mustafa Suleyman, CEO of InflectionAI and co-founder of DeepMind, models will be 10x larger than GPT-4 within a year, and we’ll see a hundredfold increase within the next 2-3 years. These predictions underscore the remarkable speed at which the field progresses.
This rapid scaling, confined to the AI labs until recently, is set to have transformative real-world impacts. The latest research suggests that these larger LLMs are on the verge of reshaping the U.S. labor market, estimating that 80% of the U.S. workforce could see at least 10% of their tasks influenced by introducing LLMs.
LLMs are rapidly shifting from research novelties to tools businesses seek. As more enterprises aim to leverage their capabilities, they find that implementing these massive models comes with a host of challenges, spanning both computational and deployment aspects. In what follows, we focus on the following questions:
- What drives the scaling of LLMs?
- What challenges and trade-offs exist in engineering LLM-based solutions?
- How can LLMs transform real-world applications?
This article explores these questions, weaving in some technical details where necessary while maintaining accessibility for a broad audience.
Scaling Laws And The Data Bottleneck
During training, a language model undergoes an iterative optimization process where its parameters are modified, or “updated,” based on the training data it's exposed to. Adjusting these numerical values shapes the model's ability to process and generate language. The scale of this operation, unparalleled in less complex AI systems, underlines the vast computational requirements of training LLMs.
However, the model's size is only half of the equation. The dataset size against which the model is trained is an equally critical aspect that influences the ultimate performance of the model. But strictly, how much data is required to train an LLM effectively?
Previous heuristics suggested that increasing the model's size was the most effective way to improve its performance, with less importance on scaling the training datasets. Recent research has reshaped this viewpoint. It determined that LLMs should be trained on much larger datasets than previously thought. This new perspective implies that the total amount of training data available might eventually become the real fundamental bottleneck for these AI systems.
The impact of scaling up LLMs transcends the predictable improvements in performance across various quantitative metrics. The true intrigue lies in the unexpected, emergent abilities that appear to suddenly "unlock" as LLMs are scaled. Examples include
- Translation between languages.
- Common-sense reasoning.
- Writing programming code.
- Solving logic puzzles.
What's really somewhat surprising is the fact that these capabilities (and many more) seem to develop through mere exposure to recurring natural language patterns during training without explicit task-specific supervision.
Do Large Language Models really "understand" the world, or just give the appearance of understanding?
What exactly lies behind this phenomenon is unclear. Can LLMs actually “understand” the world? Or, to quote Andrew Ng, do they just give the appearance of understanding (and one may further ask: is there actually a difference between the two?). The research community doesn’t seem to have a shared agreement so far.
For some experts in the field, LLMs are just giant stochastic parrots, capable of producing fluent text that can only superficially mimic the writing of an intelligent agent, yet fundamentally “dumb” and unable to reason about the world. Many other leading experts argue that, on the contrary, LLMs indirectly build internal world representations through training, allowing them to reason on disparate matters.
Commercial LLM providers and the open-source community believe further scaling these models will lead to significant new advancements. At the same time, the industry is keen on unlocking the true potential of LLMs, innovating with applications that build on their strengths. Yet, this goal can be complex. The process of integrating LLMs into real-world use cases comes with its set of technical challenges.
Why Engineering Efficient LLM-based Solutions Is Difficult
While the potential of Large Language Models (LLMs) is undeniably transformative, integrating these sizable models into practical applications presents challenges. The primary difficulties arise from the magnitude of these models, which complicates their management from both a technical and cost perspective.
Training LLMs demands significant computing power, translating to high energy consumption. Models like GPT-4 and PaLM require tens of millions of dollars merely for pre-training – the initial training phase of an LLM. Although many pre-trained LLMs are available (both open-source and proprietary, served through APIs), deploying LLMs at scale traditionally required substantial computational resources for inference.
Notably, inference costs of LLMs are becoming more manageable with time. Techniques such as leveraging Mixture-of-Experts architectures (believed to be employed by GPT-4) and new quantization methods, already applied to models like Llama, are paving the way. Such innovations allow CPU-only executions (mostly suitable for non-real-time usage), hinting at a more cost-effective horizon for LLM integration.
While model performance is typically linked to training compute, techniques exist to enhance an already trained model's capabilities or reduce its inference costs. Examples include hard-wiring LLMs with reasoning pipelines like Chain-of-Thoughts (or its refined versions).
The inference request volume largely determines the trade-off between model capabilities and deployment scale. Managing inference costs is crucial for platforms like customer service chatbots with millions of daily users, favoring compute-saving techniques. On the other hand, one can opt for smaller models in low-request environments, such as niche LLM-based tools used by relatively few users, while maximizing the expenses on inference resources. This approach can lead to cost-effective high-quality model responses.
Compute-saving techniques like model pruning can enhance a model's efficiency after training by removing irrelevant weights, yielding up to a 10x decrease in inference costs. Such methods can alter the compute-per-inference balance while maintaining performance. Unfortunately, the effectiveness of pruning can be task-dependent, and model retraining is usually required after each pruning iteration, which can greatly slow down the process.
Distillation methods are also central in the optimization process. Distillation involves training a smaller, more manageable model to mimic the behavior of a larger one. Specific recent strategies involve, for instance:
- Extracting LLM rationales as additional supervision for training small models within a multi-task framework, achieving better performance with less training data.
- Adjusting how the smaller model learns from the larger one by measuring how the larger model diverges from the smaller one, instead of the other way around. This change in perspective helps the smaller model to better understand and replicate the behavior of the larger model, leading to more accurate and higher-quality responses.
But, while distillation can mitigate cost and latency issues associated with the larger models, it introduces challenges in preserving the nuances of the larger model's responses.
Then comes the question of integrating vector database techniques. LLMs transform natural language into numerical (vector) representations, known as embeddings. Mapping not only single words but entire documents into these embedding spaces can enhance LLMs with long-term memory. Managing these embeddings effectively, particularly with vector databases, facilitates fast information retrieval, which can be a critical bottleneck in retrieval-augmented LLM integrations. It can also help ground an LLM to a “source of truth” by allowing efficient similarity search.
Simplifying LLM Complexities with LeMUR
Integrating LLMs with other tools and components presents a promising horizon for new applications and solutions. But the intricacies of managing LLMs might seem daunting to businesses wanting to capitalize on them without diving deep into the technicalities.
Herein, AssemblyAI introduces the LeMUR framework – the easiest way of extracting valuable insights from audio data with a single API call. You can easily test its capabilities in our Playground:
LeMUR integrates LLMs within the whole AI stack for spoken data. Combining Generative AI with automatic transcription and audio intelligence, it blends LLM-specific techniques, such as prompt augmentation, retrieval methods, and structured outputs in a single framework to handle spoken data efficiently.
Final Words
While large language models hold huge promise, transitioning them from research settings to real-world applications doesn’t come without some technical complexities associated with integrating and managing LLMs. We've touched upon a few in this blog post, but there are certainly more layers. Ongoing research and advancements continually provide solutions, making the deployment of LLMs more feasible and effective.
