HUME: Measuring the Human-Model Performance Gap in Text Embedding Tasks

• URL: http://arxiv.org/abs/2510.10062v2
• Date: Mon, 20 Oct 2025 16:44:59 GMT
• Title: HUME: Measuring the Human-Model Performance Gap in Text Embedding Tasks
• Authors: Adnan El Assadi, Isaac Chung, Roman Solomatin, Niklas Muennighoff, Kenneth Enevoldsen,
• Abstract summary: We introduce HUME: Human Evaluation Framework for Text Embeddings.<n>We measure human performance across 16 MTEB datasets spanning reranking, classification, clustering, and semantic textual similarity.<n>Humans achieve an average performance of 77.6% compared to 80.1% for the best embedding model.
• Score: 13.836108236883002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comparing human and model performance offers a valuable perspective for understanding the strengths and limitations of embedding models, highlighting where they succeed and where they fail to capture meaning and nuance. However, such comparisons are rarely made, as human performance on embedding tasks is difficult to measure. To fill this gap, we introduce HUME: Human Evaluation Framework for Text Embeddings. While frameworks like MTEB provide broad model evaluation, they lack reliable estimates of human performance, limiting the interpretability of model scores. We measure human performance across 16 MTEB datasets spanning reranking, classification, clustering, and semantic textual similarity across linguistically diverse high- and low-resource languages. Humans achieve an average performance of 77.6% compared to 80.1% for the best embedding model, although variation is substantial: models reach near-ceiling performance on some datasets while struggling on others, suggesting dataset issues and revealing shortcomings in low-resource languages. We provide human performance baselines, insight into task difficulty patterns, and an extensible evaluation framework that enables a more meaningful interpretation of the model and informs the development of both models and benchmarks. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.

Related papers

• Evaluating Robustness of Vision-Language Models Under Noisy Conditions [0.0176290054713643]
  Vision-Language Models (VLMs) have attained exceptional success across multimodal tasks such as image captioning and visual question answering.<n>We present a comprehensive evaluation framework to evaluate the performance of several state-of-the-art VLMs under controlled perturbations.
  arXiv  Detail & Related papers  (2025-09-15T22:31:21Z)
• A Statistical Framework for Ranking LLM-Based Chatbots [57.59268154690763]
  We propose a statistical framework that incorporates key advancements to address specific challenges in pairwise comparison analysis.<n>First, we introduce a factored tie model that enhances the ability to handle groupings of human-judged comparisons.<n>Second, we extend the framework to model covariance tiers between competitors, enabling deeper insights into performance relationships.<n>Third, we resolve optimization challenges arising from parameter non-uniqueness by introducing novel constraints.
  arXiv  Detail & Related papers  (2024-12-24T12:54:19Z)
• LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content [62.816876067499415]
  We propose LiveXiv: a scalable evolving live benchmark based on scientific ArXiv papers.<n>LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs.<n>We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities.
  arXiv  Detail & Related papers  (2024-10-14T17:51:23Z)
• DevBench: A multimodal developmental benchmark for language learning [0.34129029452670606]
  We introduce DevBench, a benchmark for evaluating vision-language models on tasks and behavioral data.<n>We show that DevBench provides a benchmark for comparing models to human language development.<n>These comparisons highlight ways in which model and human language learning processes diverge.
  arXiv  Detail & Related papers  (2024-06-14T17:49:41Z)
• Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting [0.0]
  This paper proposes a novel approach to improve embedding performance by leveraging large language models (LLMs) to enrich and rewrite input text before the embedding process. The effectiveness of this approach is evaluated on three datasets: Banking77Classification, TwitterSemEval 2015, and Amazon Counter-factual Classification.
  arXiv  Detail & Related papers  (2024-04-18T15:58:56Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• On the Compositional Generalization Gap of In-Context Learning [73.09193595292233]
  We look at the gap between the in-distribution (ID) and out-of-distribution (OOD) performance of such models in semantic parsing tasks with in-context learning. We evaluate four model families, OPT, BLOOM, CodeGen and Codex on three semantic parsing datasets.
  arXiv  Detail & Related papers  (2022-11-15T19:56:37Z)
• RuMedBench: A Russian Medical Language Understanding Benchmark [58.99199480170909]
  The paper describes the open Russian medical language understanding benchmark covering several task types. We prepare the unified format labeling, data split, and evaluation metrics for new tasks. A single-number metric expresses a model's ability to cope with the benchmark.
  arXiv  Detail & Related papers  (2022-01-17T16:23:33Z)
• Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages [10.131671217810581]
  We evaluate model robustness to out-of-domain data, modality-specific features, and languages other than English. We find that with additional image content as input, ELMo embeddings yield significantly fewer errors compared to BERT orGLoVe.
  arXiv  Detail & Related papers  (2021-04-23T18:05:52Z)

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.