Benchmarks Are Not Atomic: Composition-Aware LLM Evaluation using BenchHub

Eunsu Kim; Haneul Yoo; Guijin Son; Hitesh Patel; Amit Agarwal; Alice Oh

cs.LGMay 31, 2025

Benchmarks Are Not Atomic: Composition-Aware LLM Evaluation using BenchHub

Authors: Eunsu Kim, Haneul Yoo, Guijin Son, Hitesh Patel, Amit Agarwal, Alice Oh

Organizations: KAIST · Yonsei University · OnelineAI · Oracle

Abstract

LLM benchmarks are often treated as coherent measurement units, yet they are heterogeneous collections of instances spanning diverse domains, skills, formats, and contexts. As a result, aggregate benchmark scores can conflate model capability with benchmark composition, obscuring what existing benchmarks actually cover. We introduce BenchHub, a composition-aware evaluation framework that represents benchmarks as distributions over instance-level attributes. BenchHub integrates 54 benchmarks comprising 839K samples across 10 languages. It enables researchers to inspect benchmark contents, uncover reusable coverage hidden behind benchmark names, transparently compare benchmarks, and construct controllable evaluation sets for application-aligned model selection. Using BenchHub, we show that similarly motivated benchmarks can differ substantially in internal composition, existing benchmarks often contain reusable coverage beyond their stated purposes, models exhibit fine-grained category-level performance variation hidden by aggregate scores, and model rankings can shift under different reweighting and resampling configurations. Our results motivate evaluation practices that make benchmark composition explicit, inspectable, and controllable.

Figures & tables

Figure 1: Conceptual overview of BenchHub . BenchHub automatically merges existing benchmark datasets and annotates questions at the instance level with a unified taxonomy, making the contents of existing benchmarks explicit and reusable beyond their dataset names. This enables users to construct objective-aligned test sets and conduct fine-grained, controllable model evaluation.

Figure 2: Data distribution of all datasets used in BenchHub by coarse-grained subjects, targets, and tasks. The English and Korean data include 183k and 137k questions, respectively.

Panel of Figure 3 — Figure 3: Subject coverage distribution and alignment in English benchmarks.

Figure 4: Subject distributions of existing evaluation benchmarks.

Figure 5: LLM evaluation rankings using BenchHub (English).

Figure 6: Model pairs with similar aggregate accuracy can show distinct category-level profiles. Each point is an unordered model pair. The shaded region marks pairs within 5 %p differences in overall accuracy; red highlighted points (points A, B, C) indicate examples shown in the profile plots.

Figure 7: LLM ranking according to four sampling methods.

Rank	(a) Legal chatbot with advanced reasoning (EN)		(b) Docent agent for Korean traditional art (KO)
Rank	Customized	Stratified	Customized	Stratified
1	Qwen3-30B-A3B-Instruct-2507	Qwen3-Next-80B-A3B-Instruct	Qwen3-Next-80B-A3B-Instruct	Qwen3-Next-80B-A3B-Instruct
2	Qwen3-Next-80B-A3B-Instruct	Qwen3-30B-A3B-Instruct-2507	Qwen3-32B	Qwen3-32B
3	Olmo-3.1-32B-Instruct	Qwen3-32B	Mistral-Small-3.2-24B-Instruct-2506	Qwen3-30B-A3B-Instruct-2507
4	gemma-3-12b-it	Olmo-3.1-32B-Instruct	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct
5	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct	gemma-3-12b-it	Qwen3-14B

Table 1: Top-5 LLMs evaluated by BenchHub in real-world application scenarios.

Appendix figures & tables31 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 8 — Figure 8: User Interface of BenchHub Web Demo.

Figure 9: Average number of duplicates included in the sampling size when using the embedding-based method (Blue) and random sampling (Orange).

Hyperparameter	Value
Sequence Length	8,192
Learning Rate	$2\times 10^{-5}$
Global Batch (Effective)	256
Learning Rate Scheduler	Cosine Decay
Warmup Ratio	0.05
Training Epochs	3

Appendix

Table 2: SFT configuration details.

Sample-level Attribute	Accuracy
Subject	0.871
Skill	0.967
Cultural-specificity	0.986

Appendix

Table 3: Accuracy of fine-tuned categorizer on Qwen-2.5-7b.

Noise rate	0%	5%	10%	15%	20%	25%	30%
Random	65.07	66.07 $\pm$ 0.02	67.11 $\pm$ 0.02	68.11 $\pm$ 0.01	69.11 $\pm$ 0.01	70.10 $\pm$ 0.03	71.08 $\pm$ 0.02
Systematic	65.07	65.70 $\pm$ 0.03	66.33 $\pm$ 0.02	67.01 $\pm$ 0.04	67.65 $\pm$ 0.04	68.37 $\pm$ 0.03	68.97 $\pm$ 0.08

Appendix

Table 4: Proportion (%) of instances outside the advertised scope under random and systematic label noise.

Noise type	0%	5%	10%	15%	20%	25%	30%
Random	1.000	0.995	0.991	0.979	0.975	0.964	0.956
Systematic	1.000	0.997	0.988	0.980	0.974	0.970	0.960

Appendix

Table 5: Spearman’s

\rho

between original and perturbed model rankings, averaged across the six subject domains.

Language	G-MMLU	M-MMLU
Arabic	0.765	0.767
German	0.789	0.833
Indonesian	0.800	0.808
Kyrgyz	0.681	–
Nepali	0.709	–
Dutch	0.804	–

Appendix

Table 6: Categorizer accuracy on G-MMLU and M-MMLU.

Dataset	Reference	Cultural Specificity	Lang.	# of Samples	License
ARC	[ 10 ]	General	EN	3,548	cc-by-sa 4.0
SocialIQA	[ 63 ]	General	EN	1,954	CC-BY-4.0
WinoGrande	[ 62 ]	General	EN	1,767	Apache-2.0
Natural Questions (open)	[ 33 ]	General	EN	1,769	Apache-2.0
NarrativeQA	[ 30 ]	General	EN	10,557	Apache-2.0
TruthfulQA	[ 38 ]	General	EN	817	Apache-2.0

Appendix

Table 7: Benchmarks Included in BenchHub .

Dataset	Reference	Cultural Specificity	# of Samples	License
Language: AR
G-MMLU	[ 67 ]	General/Local	14,042	apache-2.0
ArabLegalEval	[ 20 ]	Local	15,311	-
ArabicMMLU	[ 31 ]	General/Local	14,455	cc-by-nc-sa-4.0
Language: DE
G-MMLU	[ 67 ]	General/Local	14,042	apache-2.0

Appendix

Table 8: Benchmarks Included in BenchHub -multilingual.

Panel of Figure 10 — Figure 10: Fine-grained subject distribution of all datasets used in BenchHub .

Panel of Figure 11 — Figure 11: Subject distribution of MMLU series in English, Korean, Japanese, Indonesian, and Chinese, respectively.

Panel of Figure 12 — Figure 12: Skill and cultural specificity distributions of the Holistic benchmarks and MMLU series in English, Korean, Japanese, Indonesian, and Chinese.

Figure 13: Subject distribution by benchmarks in korean benchmarks (%, $p(\text{subject}|\text{benchmark})$ ).

Panel of Figure 14 — Figure 14: Cross-benchmark overlap at cosine thresholds $\tau\in\{0{.}70,\,0{.}80,\,0{.}90\}$ . Rows index the source benchmark $A$ ; columns index the target benchmark $B$ . Cell $(A,B)$ reports the fraction of items in $A$ whose nearest neighbor in $B$ has cosine similarity at least $\tau$ .

Format		Description	Example
Binary		Two-option choice questions, typically Yes/No or True/False.	“Is the Earth flat?” → “No”
Multiple-choice QA (MCQA)		Multiple-choice question answering format.	“What is the capital of France? (A) Paris (B) Rome (C) Berlin” → (A)
Open-ended generation	Short-form	Short, direct answer generation.	“What is 2+2?” → “4”
	Free-form	Extended, explanatory answer generation.	“Explain the theory of relativity.” → “The theory of relativity, developed by Albert Einstein…”
	Open-ended	Answer generation without a definitive correct response, involving free imagination or storytelling.	“Tell a story about a journey to the moon.” → “Once upon a time, a brave astronaut set off on a magical voyage beyond the stars…”
Comparison		Subjective or preference-based evaluation of responses, typically involving agreement, helpfulness, or safety.	“Which response is more helpful?” A: “Yes.” B: “Sure, here’s how you can do that…” → (B)

Appendix

Table 9: Problem format types, descriptions, and examples.

Benchmark	Entity Being Evaluated	Problem Format
ToolHop [ 82 ]	Final answer	Short-form
ToolQA [ 87 ]	Final answer	Short-form
GPT4Tools [ 81 ]	Tool calling within output reasoning	Open-ended
ToolBench [ 15 ]	Tool calling	Short-form

Appendix

Table 10: Tool usage datasets, entity being evaluated, and problem format.

Coarse-grained	Fine-grained	Description
Science	Mathematics	The study of numbers, quantities, structures, and abstract reasoning.
	Statistics	The science of data collection, analysis, interpretation, and presentation.
	Physics	The study of matter, energy, and the fundamental forces of nature.
	Astronomy	The scientific study of celestial objects and phenomena beyond Earth.
	Chemistry	The study of substances, their properties, and how they interact and change.
	Biology	The study of living organisms and their vital processes.

Appendix

Table 11: Subject types and descriptions.

Skill	Description	Example
Knowledge	Questions that seek factual information, definitions, or explanations. Answers are usually explicitly stated or based on memorized knowledge.	"What is the capital of France?"
Reasoning	Questions that require logical thinking, problem-solving, understanding cause-effect relationships, or commonsense reasoning. Answers are not directly stated, and require interpretation or deduction.	“There are 12 apples in a box. If 4 people share them equally, how many apples does each person get?”
Value/alignment	Questions that involve value judgments, opinions, or behavioral patterns.	"I saw a grandson and their grandfather last week outside the Walmart trying to book a cab on Uber. Who was not comfortable using the phone?" (Example from [ 50 ] )

Appendix

Table 12: Skill types, descriptions, and examples.

target	Description
General	A general target without a specific cultural or national focus.
Local	A specific target toward a certain culture ( e.g ., US, KO).

Appendix

Table 13: Cultural specificity types and descriptions.

Model	% boxed	% rule-based	% other
Gemma3-4b-it	2.01	54.60	43.40
Gemma3-12b-it	93.40	5.01	1.59
Meta-Llama-3-70B-Instruct	92.51	0.00	7.49
Ministral-8B-Instruct-2410	68.94	16.23	14.84
Mistral-Small-3.2-24B-Instruct-2506	93.14	0.06	6.80
Mixtral-8x7B-Instruct-v0.1	98.81	0.56	0.64

Appendix

Table 14: Per-response extraction source on Korean datasets (each column sums to 100%). Other : no boxed substring and no rule-based parse.

Model	Before	After
gemma-3-4b-it	97.99%	1.14%
Olmo-3-1025-7B	66.77%	14.81%
Olmo-3-1125-32B	12.64%	9.28%

Appendix

Table 15: % of extraction-failure cases before and after improving the Korean extraction procedure.

Rank	(c) STEM knowledge evaluation	(d) Math teaching agent	(e) Counseling agent
1	Qwen3-Next-80B-A3B-Instruct	Qwen3-Next-80B-A3B-Instruct	Qwen3-32B
2	Qwen3-30B-A3B-Instruct-2507	Qwen3-32B	Qwen3-Next-80B-A3B-Instruct
3	Qwen3-32B	Qwen3-30B-A3B-Instruct-2507	Qwen3-30B-A3B-Instruct-2507
4	Qwen3-14B	Qwen3-14B	Qwen3-14B
5	Meta-Llama-3-70B-Instruct	Qwen3-4B	Qwen3-8B

Appendix

Table 16: Top 5 LLMs evaluated by customized BenchHub across three scenarios.

Panel of Figure 15 — Figure 15: LLM evaluation rankings using BenchHub (Korean).

Model	Subject Type						Target Type		Task Type
Model	Art & Sports	Culture	HASS	Science	Social Intel.	Tech.	Cultural	General	Knowledge	Reasoning	Value/Align.
Meta-Llama-3-70B-Instruct	54.1	62.9	68.7	72.4	54.6	70.1	60.0	68.4	72.7	56.6	63.2
Ministral-8B-Instruct-2410	30.6	38.9	37.6	49.6	27.5	44.2	36.9	41.1	43.2	39.1	29.2
Mistral-Small-3.2-24B-Instruct-2506	61.1	63.6	68.2	71.4	47.7	66.6	65.0	66.4	73.8	53.2	53.5
Mixtral-8x7B-Instruct-v0.1	58.8	59.0	63.0	65.3	45.9	60.9	54.6	61.9	68.1	47.6	53.7
OLMo-3-1025-7B	15.0	22.3	31.5	42.6	26.1	38.0	10.1	34.9	34.7	27.8	30.0

Appendix

Table 17: English accuracy (%) by model and category. Best performance in each category is bolded.

Model	Subject Type						Target Type		Task Type
Model	Art & Sports	Culture	HASS	Science	Social Intel.	Tech.	Cultural	General	Knowledge	Reasoning	Value/Align.
Meta-Llama-3-70B-Instruct	68.8	49.9	61.1	51.6	77.7	53.4	58.5	86.0	53.7	46.1	83.2
Ministral-8B-Instruct-2410	61.1	46.2	49.1	23.9	53.9	25.4	42.4	75.4	41.3	20.4	57.9
Mistral-Small-3.2-24B-Instruct-2506	68.5	52.7	63.3	48.1	68.6	47.8	56.9	75.4	54.5	42.0	73.8
Mixtral-8x7B-Instruct-v0.1	65.0	47.7	53.7	35.0	64.1	36.3	48.9	75.0	46.1	31.3	68.6
OLMo-3-1025-7B	23.5	18.4	20.3	9.9	41.0	12.1	21.3	67.3	16.7	11.8	43.6

Appendix

Table 18: Korean accuracy (%) by model and category. Best performance in each category is bolded.

Art & Sports
Category	Meta-Llama-3-70B-Instruct	Ministral-8B-Instruct-2410	Mistral-Small-3.2-24B-Instruct-2506	Mixtral-8x22B-Instruct-v0.1	Mixtral-8x7B-Instruct-v0.1	OLMo-3-1025-7B	OLMo-3-1125-32B	OLMo-3.1-32B-Instruct	Qwen3-14B	Qwen3-30B-A3B-Instruct-2507	Qwen3-32B	Qwen3-4B	Qwen3-8B	Qwen3-Next-80B-A3B-Instruct	Gemma3-12b-it	Gemma3-4b-it
Architecture	81.5	64.8	72.2	83.3	74.1	37.0	57.4	77.8	63.0	66.7	64.8	48.1	55.6	74.1	64.8	53.7
Fashion	59.1	54.5	59.1	59.1	59.1	22.7	59.1	63.6	59.1	50.0	50.0	63.6	50.0	72.7	68.2	72.7
Festivals	62.5	50.0	68.8	62.5	56.2	12.5	50.0	56.2	50.0	68.8	62.5	43.8	56.2	75.0	81.2	62.5
Music	38.3	17.7	56.2	54.9	63.0	13.0	24.2	46.2	34.5	50.8	42.7	30.2	36.4	60.9	43.2	24.2
Painting	84.6	48.1	94.2	80.8	92.3	48.1	67.3	86.5	78.8	86.5	88.5	82.7	80.8	92.3	78.8	67.3

Appendix

Table 19: English accuracy (%) by model and fine-grained subject category. Categories are grouped by coarse subject type; best performance in each row is bolded.

Art & Sports
Category	Meta-Llama-3-70B-Instruct	Ministral-8B-Instruct-2410	Mistral-Small-3.2-24B-Instruct-2506	Mixtral-8x7B-Instruct-v0.1	OLMo-3-1025-7B	OLMo-3-1125-32B	OLMo-3.1-32B-Instruct	Qwen3-14B	Qwen3-30B-A3B-Instruct-2507	Qwen3-32B	Qwen3-4B	Qwen3-8B	Qwen3-Next-80B-A3B-Instruct	Gemma3-12b-it	Gemma3-4b-it
Architecture	58.4	28.2	54.6	34.5	10.9	34.0	37.4	50.8	60.9	58.4	45.0	47.5	65.1	45.4	17.6
Fashion	43.8	17.6	37.1	33.3	7.6	24.8	29.5	41.9	50.5	51.4	33.3	36.2	53.3	39.5	23.3
Festivals	97.3	97.3	97.3	97.3	27.0	97.3	97.3	97.3	97.3	97.3	97.3	97.3	97.3	97.3	13.5
Music	89.7	85.3	90.6	88.2	28.3	85.0	87.6	90.6	90.6	91.2	86.4	88.2	92.6	89.7	18.3
Painting	91.4	89.4	96.7	93.3	38.5	92.0	91.0	93.7	94.1	96.7	91.4	92.7	96.0	93.1	24.4

Appendix

Table 20: Korean accuracy (%) by model and fine-grained subject category. Categories are grouped by coarse subject type; best performance in each row is bolded.

Model	Random	Stratified	Chatbot Arena	MixEval
Qwen3-4B	0.600	0.559	0.592	0.577
Qwen3-8B	0.624	0.577	0.601	0.598
Qwen3-14B	0.662	0.609	0.638	0.635
Qwen3-32B	0.683	0.642	0.672	0.660
Qwen3-30B-A3B-Instruct-2507	0.726	0.693	0.744	0.707
Qwen3-Next-80B-A3B-Instruct	0.734	0.704	0.727	0.720

Appendix

Table 21: Evaluation results of 15 open LLMs in English across four different sampling strategies.

Model	Random	Stratified	Chatbot Arena	MixEval
Qwen3-4B	0.557	0.550	0.497	0.533
Qwen3-8B	0.577	0.570	0.521	0.550
Qwen3-14B	0.617	0.607	0.566	0.595
Qwen3-32B	0.671	0.666	0.633	0.650
Qwen3-30B-A3B-Instruct-2507	0.642	0.639	0.606	0.623
Qwen3-Next-80B-A3B-Instruct	0.706	0.705	0.673	0.684

Appendix

Table 22: Evaluation results of 15 open LLMs in Korean across four different sampling strategies.

Model	(a)	(b)	(c)	(d)	(e)
Qwen3-4B	0.553	0.677	0.555	0.413	0.593
Qwen3-8B	0.556	0.690	0.584	0.401	0.637
Qwen3-14B	0.580	0.704	0.629	0.420	0.658
Qwen3-32B	0.605	0.795	0.687	0.468	0.684
Qwen3-30B-A3B-Instruct-2507	0.707	0.739	0.703	0.451	0.665
Qwen3-Next-80B-A3B-Instruct	0.698	0.796	0.717	0.469	0.678

Appendix

Table 23: Evaluation results of 15 open LLMs using customized BenchHub across five use cases.

Rank	BenchHub (Ours)	MATH [ 19 ]
1	Qwen/Qwen3-8B (0.35)	OLMoE-Contaminated (0.84)
2	gemma-3-4b-it (0.30)	Qwen/Qwen3-8B (0.31)
3	Llama-3.1-8B-Instruct (0.26)	gemma-3-4b-it (0.28)
4	OLMoE-Contaminated (0.22)	Llama-3.1-8B-Instruct (0.13)
5	OLMoE-base (0.16)	OLMoE-base (0.10)

Appendix

Table 24: Model ranking and accuracy across BenchHub customized for math evaluation and MATH [ 19 ] for simulation study on data contamination

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Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation

Danial Amin

LLM Evaluation Benchmark Validity

Open paper AskPDF arXiv

Benchmark scores increasingly influence the development, marketing, and selection of large language models (LLMs). Yet an overall score is interpretable only in relation to the system tested, the questions included, and the conditions of evaluation. This perspective examines five connected limitations of general LLM rankings: differences between evaluated and publicly available systems; commercial incentives and dependencies in external evaluation; benchmark saturation, defective tests, and data contamination; models exploiting scoring procedures; and the limited relevance of general scores to users' tasks. Documented cases illustrate why these problems require different responses. I argue for evaluation procedures that disclose the tested configuration, validate questions and successful task completion, report performance alongside cost and execution time, and make the scope of generalization explicit. I then discuss Isotanta, a crowdsourced benchmarking platform, as a practical example of contributed questions and repeated evaluation. A larger question pool may improve task coverage, while repeated sampling can improve the stability of estimates on that pool; neither guarantees validity or personalization. The paper distinguishes the platform's current shared ranking from proposed task-specific and user-provided evaluations. Its central argument is that model selection requires evidence about performance on the intended work, not simply a high position on a general leaderboard.

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