You don't need all the LLM benchmarks
The article discusses the redundancy of various benchmarks used to evaluate language models, suggesting that many can be skipped without losing predictive power. It highlights that a small subset of benchmarks can effectively predict performance across a wide range of tasks. The author proposes a method for selecting these benchmarks based on statistical principles, emphasizing the importance of efficient benchmarking in model evaluation.
- ▪Many language model evaluations rely on numerous benchmarks that are often highly correlated.
- ▪A small number of subjects can predict the performance of a larger set of benchmarks with high accuracy.
- ▪The article introduces a statistical approach to select the most informative benchmarks for evaluation.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Smola |
| Canonical URL | https://alex.smola.org/posts/34-benchmark-selection/ |
| Publication time | Tue, 26 May 2026 05:00:19 +0000 |
| Retrieval time | 2026-05-26T05:07:43.442Z |
| Last seen | 2026-05-26T05:07:43.442Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6yXFa1CCIq3G · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Every time a new model comes out, somebody runs it on MMLU (57 subjects), MTEB (56 tasks), HELM, the Open LLM Leaderboard, AlpacaEval, LiveBench, BigCodeBench, WildBench, Arena-Hard, MT-Bench, and a dozen others. That’s days of GPU time and a lot of human babysitting. But if you’ve ever stared at a leaderboard for ten minutes you already know the dirty secret: the columns are wildly correlated. If a model is good at one math benchmark it’s good at all of them. So how much of this can we just skip? A lot, as it turns out. On MMLU, 5 subjects out of 57 predict the remaining 52 with \(R^2 \approx 0.91\), across 5,452 models, with 10-fold cross-validation. The eigenspectrum of the score covariance tells the same story: two components capture 90% of the variance on MMLU, six on MTEB.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Smola.
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