Understanding Language Models: Insights from Sporelogic's Journey

The Beginnings of Sporelogic

Sporelogic began in 2024 as a small research outfit focused on language models. At that time, the behaviors of model outputs were not well understood. Many who followed the development of prompt engineering may remember when simply asking a model to “please think harder” was a groundbreaking discovery. Those early days were filled with excitement! Sporelogic viewed large language models (LLMs) as potential content engines. We believed they could act as system emulators, meaning they could replicate the attributes and behaviors of real entities. This perspective opened up new possibilities. It suggested that we could create complex systems by simply describing them in text.

The Core Research Question

The original question that guided our research was simple yet profound:

Given a set of input attributes, an object, and a specific context - what is the associated behavioral response distribution?

To explore this, we adopted an engineering mindset. We focused on a specific implementation concept and narrowed our problem space:

Can an emulated persona (described via attributes) replicate how a real person might respond to different situations?

This question evolved into a more straightforward inquiry:

Can we build an emulated human that can tell us whether they liked a piece of text they were exposed to?

This became our initial use case. We believed it would allow us to explore our high-level question while remaining grounded in a defined engineering problem.

Early Discoveries

Soon, we found an answer to our question: YES! With the infrastructure we built, we could create a general-purpose, system-agnostic emulator. This emulator could produce behavioral responses to various input stimuli. It was an exciting breakthrough!

However, the next question emerged:

Given the ability to emulate system responses, how accurately do these responses reflect real system behaviors?

Our original use case involved determining whether these emulated personas could express opinions about text. They could, but their validity depended on their similarity to real targets. To test this, we benchmarked the performance of our emulated personas against historical A/B test data.

Performance Benchmarking

We characterized the performance of several emulation architectures and found accuracy generally ranged from 52% to 60%. This was only marginally better than random chance. It’s important to note that these numbers represent the ‘lower bound’ of our technology's capabilities. We believe more complex modeling could improve these figures, but we decided to set those topics aside for later exploration.

At this point, we had two choices: continue learning about improving language model-driven emulation techniques or put on our engineering hats to solve the original problem. We chose the latter.

Traditional Machine Learning Approaches

We employed traditional machine learning algorithms and large datasets. This approach allowed us to develop models that could predict A/B test winners with an accuracy between 76% and 92%, depending on how we chunked the data. We were pleased with these results.

After this process, everything began to make intuitive sense:

• Large datasets exist on headline A/B tests and their outcomes.

• Well-studied semantic patterns impact engagement.

• These patterns can be found in our datasets, differentiating high-performing from low-performing variants.

• We can train models to leverage these patterns to predict engagement outcomes.

  
  

The Limitations of Language Models

None of this required a language model. Using LLMs is often costlier and slower. They also come with the significant overhead of managing noise unrelated to the relationships between semantic features and their outcomes. This situation illustrates a scenario where language models do not perform as well as classic function approximation.

From our experiments, we learned that while language model-based applications are powerful content generators, they do not provide the same predictive accuracy achievable through traditional machine learning methods for our use case. This observation is not meant to diminish the value of language models as engineering tools. Instead, it serves as a reminder to understand your problem fully rather than simply applying an LLM.

Future Directions

We plan to continue exploring the language model space. We hope to release more details about our findings soon. For those interested in these topics, we are preparing a couple of technically detailed follow-ups to this blog. One will delve deeper into the original thesis of our company, while another will present the specific experimental results that this blog is based on. Stay tuned!

In conclusion, our journey at Sporelogic has been enlightening. We have gained valuable insights into the capabilities and limitations of language models. Our commitment to understanding these tools will guide us as we move forward in our research and development efforts.

For more information on our work and insights, feel free to check out our website at Sporelogic.