MiniLongBench: Efficient LCU Benchmark

• MiniLongBench is a low-cost benchmark for long context understanding in LLMs that uses a three-stage compression pipeline to reduce evaluation time by about 95% while retaining high ranking fidelity.
• It integrates PCA-based dimensionality reduction, logistic regression modeling, and K-Means clustering to compress the original LongBench dataset to 237 representative samples.
• Empirical evaluations demonstrate a near-perfect Spearman rank correlation (≈0.97) with full-scale benchmarks, lowering computational demands to just 4.5% of the original cost.

MiniLongBench is a low-cost benchmark devised for evaluating long context understanding (LCU) in LLMs. By leveraging a principled data compression pipeline, MiniLongBench retains the core evaluation fidelity of the comprehensive LongBench dataset while reducing evaluation time and computational demands by approximately 95%. MiniLongBench comprises 237 test samples across six major task categories and 21 distinct subtasks and demonstrates near-perfect rank correlation with LongBench outcomes, thus enabling rapid and practical benchmarking of LCU capabilities in modern LLMs (Huang et al., 26 May 2025).

1. Rationale for MiniLongBench and the LCU Benchmarking Bottleneck

Long context understanding (LCU) has become a central theme in LLM research, motivated by real-world tasks such as book-length question answering, multi-page document summarization, and repository-level code completion, which require context windows extending to thousands or tens of thousands of tokens. The LongBench benchmark, introduced by Bai et al. (2024), consolidates 4,750 long-text test instances (average length: 6,711 English words or 13,386 Chinese characters) across various LCU categories. Evaluating a single LLM on LongBench typically requires 15–30 hours using 8× RTX3090 GPUs, creating a prohibitive barrier for rapid model iteration, architecture search, and tuning due to both cost and time requirements.

A large-scale random-sampling analysis conducted during the development of MiniLongBench showed that randomly discarding up to 95% of LongBench samples often preserved a moderate to strong Spearman rank correlation (Sp≥0.6–0.8) with the full-benchmark model ranking. This redundancy highlighted an opportunity to design a far more efficient, yet still reliable, benchmark (Huang et al., 26 May 2025).

2. Compression Pipeline for Efficient LCU Evaluation

MiniLongBench is obtained through a three-stage compression strategy specifically tailored to the statistical characteristics of long-text, sparse information LCU datasets.

2.1 Data Encoding and Dimensionality Reduction

Each long-text sample $s_j$ is first embedded using a fixed text embedding model (OpenAIEmbedding). To manage the embedding vectors’ high dimensionality (often 1024 features), principal component analysis (PCA) is used to reduce dimensionality to $d=10$, forming dense, low-rank summary vectors $e_j \in \mathbb{R}^d$:

$$e_j = \mathrm{PCA}_d\bigl[\kappa_{\mathrm{text}}(s_j)\bigr],\quad j=1,\dots,|S_L|.$$

2.2 Performance-Based Representation Learning

Let $m$ LLMs $\ell_i$ be deployed as “probes.” For each model–sample pair $(\ell_i, s_j)$, a binary performance label $y_{i,j}$ is computed by thresholding a normalized metric (such as F1, Rouge-L, or edit similarity):

$$y_{i,j} = \mathbf{1}[\mathrm{metr}(\ell_i,s_j)\ge c] \in \{0,1\},$$

with the threshold $c$ selected to minimize discretization error over all entries. The probability of correct answer is parameterized by a logistic regression:

$d=10$0

Here, $d=10$1 models the “profile” of $d=10$2, and $d=10$3 encodes sample difficulty.

2.3 Clustering for Sample Selection

Upon training convergence, every sample $d=10$4 has a learned embedding $d=10$5 in $d=10$6. These vectors are partitioned using K-Means clustering, where $d=10$7 and $d=10$8 specifies the compression ratio. Only cluster centers are retained, resulting in a pruned, yet representative, MiniLongBench.

3. Benchmark Composition and Task Coverage

MiniLongBench preserves the structure and diversity of LongBench through six task categories comprising 21 subtasks:

Category	Datasets/Subtasks Include	Original Count	Pruned Count
Single-Document QA	NarrativeQA, Qasper, MultiFieldQA-en/zh	Various	Significantly reduced
Multi-Document QA	HotpotQA, 2WikiMultihopQA, MuSiQue, DuReader	Various	Significantly reduced
Summarization	GovReport, QMSum, MultiNews, VCSUM	Various	Significantly reduced
Few-Shot Learning	TREC, TriviaQA, SAMSum, LSHT	Various	Significantly reduced
Synthetic Tasks	PassageCount, PassageRetrieval-en/zh	Various	Significantly reduced
Code Completion	LCC, RepoBench-P	Various	Significantly reduced

From the 4,750 original test items, MiniLongBench selects $d=10$9 samples—a per-task reduction of approximately 93–98%. Clustering in a performance-based embedding space, rather than random sampling, allows retention of the benchmark's diversity and challenge spectrum.

4. Empirical Performance, Fidelity, and Efficiency

MiniLongBench was validated using recorded performance data from over 60 LLMs, with $e_j \in \mathbb{R}^d$0 included in the training/compression stage and the remainder held out for analysis. Evaluation strategies included:

• Direct benchmarking of MiniLongBench (237 samples) vs. full LongBench ($e_j \in \mathbb{R}^d$1): median Spearman’s rank correlation Sp ≈ 0.95.
• Predictive evaluation using fine-tuned $e_j \in \mathbb{R}^d$2-vectors on MiniLongBench and scoring the full LongBench set per

$e_j \in \mathbb{R}^d$3

yielding Sp ≈ 0.97.

Due to an equivalent average sample length (∼6,200 English words, 10,300 Chinese characters), the computational requirements for using MiniLongBench reside at 4.5% of the original. On an 8× RTX3090 configuration, a task requiring ~20 hours on LongBench is reduced to ~1 hour on MiniLongBench.

5. Implications, Limitations, and Ongoing Work

MiniLongBench substantially lowers the cost and feasibility barrier for LCU benchmarking in LLM research while maintaining model ranking fidelity (Sp = 0.97 vs. LongBench). This enables more rapid assessment of new architectures and training paradigms.

Limitations include:

• The necessity of an initial pool of LLMs and collection of their binary performance records, which remains resource-intensive.
• The high but not perfect Spearman correlation, with residual bias most evident in summarization and synthetic subtasks.
• Manual selection of probe models ($e_j \in \mathbb{R}^d$4) could be superseded by automated, diversity-driven methodologies.

Future directions suggested by the authors involve exploring higher compression ratios ($e_j \in \mathbb{R}^d$5) to further reduce benchmark size, developing advanced methods for diverse and principled probe model selection, and extending the general approach to generation-oriented long-text benchmarks beyond QA and summarization (Huang et al., 26 May 2025).

6. Research Impact and Prospects

MiniLongBench demonstrates that the redundancy inherent in comprehensive long-context benchmarks can be systematically exploited to enable ultra-efficient LCU evaluation while preserving the integrity of ranking signals necessary for scientific progress. Its open-source release (https://github.com/MilkThink-Lab/MiniLongBench) is positioned to facilitate reproducible, low-cost LLM assessment and interval licensure for exploration of novel LCU-centric architectures and algorithms. Notably, the framework provides an empirically grounded route to generate similar condensed benchmarks in adjacent domains. As benchmark construction for LLMs continues to evolve, the principles underpinning MiniLongBench are likely to inform best practices in efficient, scalable, and reliable evaluation design (Huang et al., 26 May 2025).