I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
Agar is prized among chefs for its ability to form firm, heat-stable gels at remarkably low concentrations — typically just 0.5-2 percent by weight. Culinary agar is available as powder, flakes, strips, or blocks, and makes up about 90 percent of the global use of agar. Unlike gelatine, which melts at body temperature, agar gels remain solid up to about 185°F (85°C), making it ideal for setting dishes served at room temperature or warmer. It is also flavorless and odorless, vegan and halal, and can create both delicate jellies and firm aspics. Yet, while increasingly employed in kitchens worldwide, agar had not yet entered the laboratory.
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One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.
"It's a period where the history is not yet written," says Dr Seaman.
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