PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs

• URL: http://arxiv.org/abs/2510.06730v1
• Date: Wed, 08 Oct 2025 07:37:19 GMT
• Title: PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
• Authors: Manuel Frank, Haithem Afli,
• Abstract summary: We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that generates meaning paraphrases at evaluation time and aggregates results across multiple runs.<n>We validate our hypothesis that the performance of sentence encoders is sensitive to changes in token space even when semantics remain fixed.<n>Our results are statistically robust over multiple runs and we extended our experiments to 3 datasets covering 10 languages.
• Score: 0.21485350418225244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current evaluations of sentence embedding models typically rely on static test beds such as the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can inflate reported performance and obscure real-world robustness. We introduce the Paraphrasing Text Embedding Benchmark (PTEB), a dynamic protocol that stochastically generates meaning-preserving paraphrases at evaluation time and aggregates results across multiple runs. Using a cost-efficient LLM-based method grounded in semantic textual similarity gold ratings, we show that LLMs generate token-diverse but semantically preserving, paraphrases. Across 7 MTEB tasks, we validate our hypothesis that the performance of sentence encoders is sensitive to changes in token space even when semantics remain fixed. We also observe that smaller models are not disproportionately affected relative to larger ones. Our results are statistically robust over multiple runs and we extended our experiments to 3 multilingual datasets covering 10 languages. More generally, we aim to propose a new evaluation paradigm in NLP that relies less on static, pre-defined benchmarks but shifts towards dynamic, stochastic evaluation leveraging eval-time compute.

Related papers

• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• CMT-Bench: Cricket Multi-Table Generation Benchmark for Probing Robustness in Large Language Models [11.167804698594866]
  We present CMT-Bench, a diagnostic benchmark built from live cricket commentary.<n>We find large drops without extractive summaries, monotonic degradation with input length, and consistent accuracy drop under entity-form changes.
  arXiv  Detail & Related papers  (2025-10-20T23:51:28Z)
• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
  Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
  arXiv  Detail & Related papers  (2025-08-18T17:59:15Z)
• Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles [27.216039759668675]
  We identify distinct writing styles that consistently trigger either low or high performance across a range of models and tasks.<n>Our work offers a scalable approach to augment existing benchmarks, improving the external validity of the assessments they provide for measuring LLM performance.
  arXiv  Detail & Related papers  (2025-07-29T18:59:09Z)
• Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation [0.0]
  In this paper, we compare Czech-specific and multilingual sentence embedding models through intrinsic and extrinsic evaluation paradigms.<n>For intrinsic evaluation, we employ Costra, a complex sentence transformation dataset, and several Semantic Textual Similarity (STS) benchmarks to assess the ability of the embeddings to capture linguistic phenomena.<n>In the extrinsic evaluation, we fine-tune each embedding model using COMET-based metrics for machine translation evaluation.
  arXiv  Detail & Related papers  (2025-06-25T07:46:17Z)
• Beyond the Singular: The Essential Role of Multiple Generations in Effective Benchmark Evaluation and Analysis [10.133537818749291]
  Large language models (LLMs) have demonstrated significant utilities in real-world applications.<n> Benchmark evaluations are crucial for assessing the capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-02-13T03:43:33Z)
• Robustness of Large Language Models to Perturbations in Text [2.2734015467359217]
  Large language models (LLMs) have shown impressive performance, but can they handle the inevitable noise in real-world data?<n>This work tackles this critical question by investigating LLMs' resilience against morphological variations in text.<n>Our findings show that contrary to popular beliefs, generative LLMs are quiet robust to noisy perturbations in text.
  arXiv  Detail & Related papers  (2024-07-12T04:50:17Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• Generating Benchmarks for Factuality Evaluation of Language Models [61.69950787311278]
  We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation.
  arXiv  Detail & Related papers  (2023-07-13T17:14:38Z)
• 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)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• Just Rank: Rethinking Evaluation with Word and Sentence Similarities [105.5541653811528]
  intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. We propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks.
  arXiv  Detail & Related papers  (2022-03-05T08:40:05Z)

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.