Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

• URL: http://arxiv.org/abs/2609.12475v1
• Date: Fri, 11 Sep 2026 06:15:25 GMT
• Title: Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models
• Abstract summary: We present ZipBench, a simple and low-cost benchmark suite for large language models (LLMs)<n>ZipBench evaluates only a small set of anchor LLMs, synthesizes pseudo evaluation results to broaden coverage, learns compact sample representations, and selects a small yet representative subset.<n>ZipBench Zoo, a collection of compact versions of 100+ benchmark proxies spanning text, multimodal, and agent tasks, achieves mean absolute errors of 0.002--0.02 and average Spearman correlations of 0.98 with the full benchmarks.
• Score: 27.700132797675007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Although recent benchmark compression methods (BCMs) can mitigate this cost, many strong BCMs rely on large collections of per-sample evaluation results from numerous LLMs to identify representative samples. Building such collections is also expensive unless they are already public, making these methods difficult to extend to newly released benchmarks. To address this challenge, we present ZipBench, a simple and low-cost BCM with theoretical error and rank-consistency guarantees. ZipBench evaluates only a small set of anchor LLMs, synthesizes pseudo evaluation results to broaden coverage, learns compact sample representations, and selects a small yet representative subset. Building on it, we create ZipBench Zoo, a collection of compact versions of 100+ benchmark proxies spanning text, multimodal, and agent tasks. These benchmark achieve mean absolute errors of 0.002--0.02 and average Spearman correlations of ~0.98 with the full benchmarks. Overall, ZipBench reduces the cost of both LLM evaluation and compact benchmark construction, lowering the barrier to broad LLM research for compute-constrained researchers. The code has been released in https://github.com/MilkThink-Lab/ZipBench.

Related papers

• SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers [55.39407031861402]
  We introduce SCDBench, a dataset and benchmark methodology for smart contract decompilation.<n>The dataset contains 600 real-world Solidity contracts with paired bytecode inputs, ground-truth source code, and replayable semantic checkpoints.<n>We evaluate Claude Opus 4.7, GPT-5.3-Codex, and GLM-5 in a zero-shot decompilation setting, including GLM-5 variants with and without extended reasoning.
  arXiv  Detail & Related papers  (2026-05-27T20:08:47Z)
• DEP: A Decentralized Large Language Model Evaluation Protocol [51.3646001384887]
  Decentralized Evaluation Protocol (DEP) is a decentralized yet unified and standardized evaluation framework.<n>By decoupling users, LLMs, and benchmarks, DEP enables modular, plug-and-play evaluation.<n>We develop DEP Toolkit, a protocol-compatible toolkit that supports features such as breakpoint resume, concurrent requests, and congestion control.
  arXiv  Detail & Related papers  (2026-03-01T16:10:16Z)
• Rethinking LLM Evaluation: Can We Evaluate LLMs with 200x Less Data? [82.09573568241724]
  EssenceBench is a coarse-to-fine framework utilizing an iterative Genetic Algorithm (GA)<n>Our approach yields superior compression results with lower reconstruction error and markedly higher efficiency.<n>On the HellaSwag benchmark (10K samples), our method preserves the ranking of all models shifting within 5% using 25x fewer samples, and achieves 95% ranking preservation shifting within 5% using only 200x fewer samples.
  arXiv  Detail & Related papers  (2025-10-12T05:38:10Z)
• How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework [8.76693832650115]
  Overestimation in evaluating large language models (LLMs) has become an increasing concern.<n>We propose ArxivRoll, a dynamic evaluation framework inspired by one-time pad encryption in cryptography.
  arXiv  Detail & Related papers  (2025-07-25T12:39:03Z)
• LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient [19.673388630963807]
  We propose an automated and unbiased evaluation framework, structured around four dimensions and ten criteria.<n>Under this framework, we analyze the advantages and weaknesses of directly prompting large language models (LLMs) as generic benchmark generators.<n>We then introduce a series of methods to address the identified weaknesses and integrate them as BenchMaker.<n>Experiments confirm that BenchMaker achieves superior or comparable performance to human-annotated benchmarks on all metrics.
  arXiv  Detail & Related papers  (2025-02-02T06:36:01Z)
• metabench -- A Sparse Benchmark of Reasoning and Knowledge in Large Language Models [5.972993094932516]
  Large Language Models (LLMs) vary in their abilities on a range of tasks.<n>We show that there exists a small set of common underlying abilities that these benchmarks measure.<n>We distill a sparse benchmark, metabench, that has less than 3% of the original size of all six benchmarks combined.
  arXiv  Detail & Related papers  (2024-07-04T17:57:38Z)
• MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures [57.886592207948844]
  We propose MixEval, a new paradigm for establishing efficient, gold-standard evaluation by strategically mixing off-the-shelf benchmarks. It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks.
  arXiv  Detail & Related papers  (2024-06-03T05:47:05Z)
• tinyBenchmarks: evaluating LLMs with fewer examples [42.95407654805037]
  We release evaluation tools and tiny versions of popular benchmarks: Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0. Our empirical analysis demonstrates that these tools and tiny benchmarks are sufficient to reliably and efficiently reproduce the original evaluation results.
  arXiv  Detail & Related papers  (2024-02-22T22:05:23Z)
• Linear-time Minimum Bayes Risk Decoding with Reference Aggregation [52.1701152610258]
  Minimum Bayes Risk (MBR) decoding is a text generation technique that has been shown to improve the quality of machine translations. It requires the pairwise calculation of a utility metric, which has quadratic complexity. We propose to approximate pairwise metric scores with scores calculated against aggregated reference representations.
  arXiv  Detail & Related papers  (2024-02-06T18:59:30Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.