Lex

@notesbylex.com

Senior MLE at @Canva. Full-stack developer. Talks about Software dev, LLMs, Agentic Reasoning, Obsidian, note-taking, dog photos.

LLMs are still the wrong tool for tabular data "no amount of prompt polishing aimed at for- mat, precision, or batching should be expected to fix high-dimensional in-context prediction, and purpose-built tabular models remain the right tool" arxiv.org/abs/2608.02412

Why Large Language Models Fail at Tabular Prediction

Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the most common machine learning workloads:...

arxiv.org

Published a summary of the paper "Ask, Don’t Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement". It outlines BinEval - an LLM-Judge approach that decomposes scores into binary subquestions, making them interpretable and actionable. notesbylex.com/decomposing-...

Decomposing LLM Judge Scores Into Yes/No Questions

Most LLM judges output a single score for each criterion. However, a score can often be opaque and difficult to act on, especially at the higher end: what should you change to get a 5 instead...

notesbylex.com

Learning to Reason without External Rewards (aka Self-confidence Is All You Need) Turns out we can just use the LLM's internal sense of confidence as the reward signal to train a reasoning model, no reward model / ground-truth examples / self-play needed. Amazing. https://notesbylex.com/learning…