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Introducing The Inferential: How to Effectively Sharpen Your Intuition

It's harder to brush up on conceptual understanding than on coding skills. Here's how The Inferential leverages spaced repetition, Bayesian modeling, and adaptive scheduling to help practitioners keep their intuition sharp.

Introducing The Inferential: How to Effectively Sharpen Your Intuition

It's harder to brush up on conceptual understanding than on coding skills. Here's how The Inferential leverages spaced repetition, Bayesian modeling, and adaptive scheduling to help practitioners keep their intuition sharp.

Articles

Jev: Zero-Shot Classification Without the Text Generation

A lot of people use LLMs and zero-shot classification to make decisions, but that takes a lot of compute, and it can be really slow when you're working with a large corpus. Let's take a look a Jev — a new zero-shot classification model that's cheaper and faster than using an LLM.

Jev: Zero-Shot Classification Without the Text Generation

A lot of people use LLMs and zero-shot classification to make decisions, but that takes a lot of compute, and it can be really slow when you're working with a large corpus. Let's take a look a Jev — a new zero-shot classification model that's cheaper and faster than using an LLM.

The Inferential: What to Expect

The Inferential will be publishing original, considered writing about practical data science and machine learning topics, aimed at practitioners.

You don’t always need a frontier model

Frontier LLMs are capable. But they're also expensive, slow, and prone to hallucination. For many NLP tasks, using a fine-tuned BERT model will be more accurate, easier to audit, better suited to your domain, and cheaper by orders of magnitude — and it'll keep your data off someone else's servers.

How Much of an Advantage do Host Countries Have at the World Cup?

The consensus is that home field advantage matters a lot in football. But how much does it matter at the World Cup? How much of an advantage do host countries have? Does being a host cause a country to do better? Let's estimate the causal effect of hosting on tournament performance.

How Much of an Advantage do Host Countries Have at the World Cup?

The consensus is that home field advantage matters a lot in football. But how much does it matter at the World Cup? How much of an advantage do host countries have? Does being a host cause a country to do better? Let's estimate the causal effect of hosting on tournament performance.

Does Extra Time Matter? Evidence from 90 Years of World Cups

Extra time has a reputation for being cagey and boring — that it's just two exhausted teams trying not to lose. You often see people argue that we should just skip it and go straight to penalties. But contrary to its reputation, just as many goals are scored per 90 minutes as in regulation play. And it's decisive half the time.

Does Extra Time Matter? Evidence from 90 Years of World Cups

Extra time has a reputation for being cagey and boring — that it's just two exhausted teams trying not to lose. You often see people argue that we should just skip it and go straight to penalties. But contrary to its reputation, just as many goals are scored per 90 minutes as in regulation play. And it's decisive half the time.

Making a Monolingual Model Bilingual with Domain Adaptation

You have an English BERT model that works well on legal text. But your corpus is bilingual. Here's how domain adaptation on a bilingual corpus can produce a model with strong masked language modeling performance in both languages — and why legal text makes this work better than you might expect.

Domain Adaptation or Fine Tuning?

Fine-tuning and domain adaptation are often used interchangeably, but they solve different problems and require different approaches. Getting the distinction wrong is one of the more expensive mistakes in applied NLP.

Why General-Purpose Language Models Struggle with Legal Text

Legal language has structural and semantic properties that general-purpose models weren't trained to handle. For high-stakes legal NLP applications, the choice between fine-tuning, domain adaptation, and prompting is a consequential engineering decision.

Why General-Purpose Language Models Struggle with Legal Text

Legal language has structural and semantic properties that general-purpose models weren't trained to handle. For high-stakes legal NLP applications, the choice between fine-tuning, domain adaptation, and prompting is a consequential engineering decision.

Can You Tell If Something Was Written by an LLM?

The "em-dash debate" misses the point entirely. Detecting LLM-generated text is a challenging classification problem, and the "folk methods" people use to do it are somewhere between unreliable and useless.

Can You Tell If Something Was Written by an LLM?

The "em-dash debate" misses the point entirely. Detecting LLM-generated text is a challenging classification problem, and the "folk methods" people use to do it are somewhere between unreliable and useless.

Want a Good Model? Start with a Good Measurement Strategy

Measurement strategy is the most consequential modeling decision in a supervised learning project. Treat labeling as a low-skill data-cleaning task and you'll build a model that learns the wrong thing. You can't gloss over measurement and validity and expect to build a good model.

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