Pretraining on the Test Set Is All You Need

2023/09/13 by Rylan Schaeffer, Schaeffer, Rylan · 16 voices · 7 citations
#cs.CL #cs.AI

paper · pdf · doi:10.48550/arxiv.2309.08632

Abstract

Inspired by recent work demonstrating the promise of smaller Transformer-based language models pretrained on carefully curated data, we supercharge such approaches by investing heavily in curating a novel, high quality, non-synthetic data mixture based solely on evaluation benchmarks. Using our novel dataset mixture consisting of less than 100 thousand tokens, we pretrain a 1 million parameter transformer-based LLM phi-CTNL (pronounced ``fictional") that achieves perfect results across diverse academic benchmarks, strictly outperforming all known foundation models. phi-CTNL also beats power-law scaling and exhibits a never-before-seen grokking-like ability to accurately predict downstream evaluation benchmarks' canaries.

Cited by

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• Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
• Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs
• Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks

Discussions

• Pretraining on the Test Set Is All You Need [hn, 68 points, 26 comments]
• I can't believe this paper only has 35 citations and people keep trying to rewrite it arxiv.org/abs/2309.08632 [bsky, 39 points, 4 comments]
• ну во первых это красиво [bsky, 5 points, 2 comments]
• Pretraining on the Test Set Is All You Need [hn, 4 points, 0 comments]
• Big if true [bsky, 4 points, 0 comments]
• Pretraining on test set is all you need [hn, 3 points, 1 comments]
• Pretraining on the Test Set Is All You Need arxiv.org/abs/2309.08632 くそわろてる [bsky, 2 points, 1 comments]
• Pretraining on the Test Set Is All You Need [hn, 2 points, 0 comments]
• You only need 64 A100s to pretrain on the test set. arxiv.org/abs/2309.08632 [bsky, 2 points, 0 comments]
• Pretraining on the Test Set Is All You Need [hn, 1 points, 0 comments]
• 📝 Pretraining on the Test Set Is All You Need 📚👾 "\textbf{phi-CTNL} is trained as a causal language model on a mixture of carefully curated academic benchmarks and is then evaluated as a masked lan [mastodon, 0 points, 0 comments]
• GenAI in a nutshell. arxiv.org/abs/2309.08632 [bsky, 0 points, 0 comments]
• I mean, have you even tried pre-training on the questions + answer key? (The paper is satire but highlights the problem of test set data contamination in most/all LLM benchmarks) arxiv.org/abs/2309.08 [bsky, 0 points, 0 comments]
• Classic arxiv.org/abs/2309.08632 [bsky, 0 points, 0 comments]
• I have been doing ML wrong my whole life: arxiv.org/abs/2309.08632 [bsky, 0 points, 0 comments]
• after all... arxiv.org/abs/2309.08632 [bsky, 0 points, 0 comments]

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