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arxiv: 2605.22544 · v1 · pith:Q24F4WTDnew · submitted 2026-05-21 · 💻 cs.CL · cs.IR

One prompt is not enough: Instruction Sensitivity Undermines Embedding Model Evaluation

Yevhen Kostiuk , Kenneth Enevoldsen This is my paper

Pith reviewed 2026-05-22 06:26 UTC · model grok-4.3

classification 💻 cs.CL cs.IR
keywords prompt sensitivityembedding modelsinstruction tuningbenchmark evaluationmodel rankingsperformance variationtask instructionsrobustness testing
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The pith

Embedding model rankings can be made to favor any participant simply by changing the evaluation prompt.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that instruction-tuned embedding models change their measured performance when the same task is described with different wording, so a single fixed prompt cannot give a trustworthy picture of how well any model works. The authors test this by running six models on eleven datasets with fifteen distinct prompts each, producing 990 separate evaluations that expose wide score spreads. If the finding holds, then current leaderboards systematically misrepresent real capability and let rankings be rearranged at will through prompt selection. Practitioners who rely on these models for search or retrieval therefore face unpredictable results once users start writing their own instructions.

Core claim

Instruction embedding models exhibit large performance differences across plausible task instructions for the same dataset. When the authors evaluate six models over fifteen prompts per dataset, the default single prompt used in existing benchmarks can either understate or overstate results relative to the full distribution, and any model can be moved into first place on the leaderboard by selecting favorable prompts.

What carries the argument

The spread of retrieval or similarity scores that each model produces when the identical task is rephrased in fifteen different ways.

If this is right

  • Single-prompt scores reported in existing benchmarks do not reflect the range of outcomes users will actually observe.
  • Any of the six models can be placed at the top of the ranking by choosing the best prompt for each task.
  • Future benchmarks must either average across many prompts or publish sensitivity numbers next to point estimates.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training procedures for these models could be adjusted to reduce dependence on exact wording rather than chasing peak scores on one prompt.
  • The same sensitivity pattern is likely to appear in other instruction-following systems beyond embedding models.
  • Application developers may need to test several prompt variants before trusting a published leaderboard score for their use case.

Load-bearing premise

The fifteen prompts written for each dataset stand in for the full range of instructions that actual users would write.

What would settle it

Re-running the full set of experiments on a much larger collection of prompts drawn from real user logs and finding that model orderings remain stable across that wider sample.

Figures

Figures reproduced from arXiv: 2605.22544 by Kenneth Enevoldsen, Yevhen Kostiuk.

Figure 1
Figure 1. Figure 1: Overview of our approach, showing the currently unreported distribution of performances over reasonable prompts against the point estimate that is the reported results. Below that we show three possible option, either the reported result is representative of the distribution, it is lower than the (prompt deflation) or higher than (prompt inflation). offering higher flexibility for various domains and objec… view at source ↗
Figure 2
Figure 2. Figure 2: Example of prompt deflation and inflation on a sample of evaluated tasks and models. For all the scores and distributions see Appendix A. the prompt within the models implementation – to allow customization to a specific prompt scheme – making it possible for the model developers to selectively choose the best performing prompts for a given task. Current research on prompt sensitivity is fo￾cused mostly on… view at source ↗
Figure 3
Figure 3. Figure 3: Prompt sensitivity: Coefficient of variance distributions across the models and prompts. We see that certain models have a high degree of variance across prompts. (Kwon et al., 2023) structured outputs with the default parameters. The model was instructed to generate prompts based on the combination of language, task description, and task name. The prompts were manually evaluated to ensure that they were c… view at source ↗
Figure 4
Figure 4. Figure 4: Leaderboard sensitivity under adversarial prompt selection. We show for each model two scenarios. Left: The model uses its best prompt while others use the default. Right: The model uses its best prompt while others use their worst. Ranks are computed via Borda scores across all tasks. In both cases we compare against the default configuration (MTEB Reported). Under default prompts, KaLM and Qwen3 oc￾cupy … view at source ↗
Figure 5
Figure 5. Figure 5: Probability of prompt inflation, i.e. reported score higher than the median, across tasks. The uncer￾tainty is a 95% bootstrapped confidence interval. 4 Conclusion In this paper, we conducted an empirical study of prompt sensitivity across 165 task-specific prompts. We find that reported benchmark scores frequently misrepresent expected performance: the MTEB de￾fault prompt can systematically overstate or … view at source ↗
Figure 6
Figure 6. Figure 6: Detailed scores per model, metric, and task. For some tasks MTEB does not currently report a score hence [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of human vs machine generated prompt score distribution for Classification and Retrieval [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of human vs machine generated prompt score distribution for Clustering and STS tasks. Red [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
read the original abstract

Instruction embedding models have become common among state-of-the-art models, however are evaluated using a single prompt per task. The single-point evaluation ignores a main problem of the instruction-based approach namely: sensitivity to the phrasing of the instruction. We present an empirical study of prompt sensitivity across 6 embedding models, 11 datasets, and 15 task-specific prompts per dataset, a total of 990. We show that reported scores misrepresent the distribution of scores over plausible prompts. The default prompt can both systematically understate or overstate performance. Furthermore, we show that the leaderboard ranking is not robust to prompt selection: by choosing prompts favorably, any model in our study can be promoted to first place. Our findings suggest that single-prompt evaluation is insufficient for instruction-tuned embedding models and that benchmarks should incorporate prompt robustness, either by evaluating over multiple prompts or by reporting sensitivity alongside point estimates.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims that instruction-tuned embedding models are highly sensitive to prompt phrasing, making single-prompt evaluations unreliable. Through experiments on 6 models across 11 datasets using 15 task-specific prompts each (990 total evaluations), it shows that default prompts can understate or overstate performance and that leaderboard rankings are not robust—any model can be made to rank first with favorable prompt choices. The authors argue that benchmarks should incorporate prompt robustness via multi-prompt evaluation or sensitivity reporting.

Significance. If the empirical observations hold, this work is significant for highlighting a systematic weakness in current evaluation practices for instruction-based embedding models. The scale of the study (990 runs) provides concrete evidence of rank instability, which could prompt the community to adopt more robust benchmarking standards. The purely empirical nature with no circular derivations strengthens the reporting of observed score distributions and reversals.

major comments (1)
  1. The central claim that any model can be promoted to first place (and thus that single-prompt evaluation undermines leaderboards) depends on the 15 prompts per dataset capturing enough variation to reflect real evaluation fragility. The manuscript provides no external validation of the prompt set against actual user instructions, crowdsourced phrasings, or linguistic diversity metrics, leaving open whether the rank reversals are an artifact of the prompt-generation procedure rather than a general property of these models.
minor comments (2)
  1. Add explicit details on the statistical testing used to support claims about score misrepresentation and rank changes, as the current description leaves verification of variance and significance unclear.
  2. Clarify the exact method for generating the 15 task-specific prompts (templates, manual variation, etc.) to allow reproducibility and assessment of coverage.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the significance of our empirical findings. We address the major comment below.

read point-by-point responses

  1. Referee: The central claim that any model can be promoted to first place (and thus that single-prompt evaluation undermines leaderboards) depends on the 15 prompts per dataset capturing enough variation to reflect real evaluation fragility. The manuscript provides no external validation of the prompt set against actual user instructions, crowdsourced phrasings, or linguistic diversity metrics, leaving open whether the rank reversals are an artifact of the prompt-generation procedure rather than a general property of these models.

    Authors: We agree that external validation of the prompt set (e.g., via crowdsourcing or linguistic diversity metrics) would strengthen the generalizability of our results. Our 15 prompts were manually authored to span common variations in length, specificity, formality, and structure drawn from model documentation and typical user queries for embedding tasks. The consistent observation of rank instability across all six models and eleven datasets indicates that sensitivity is not limited to an idiosyncratic prompt set. We will revise the manuscript to (1) provide a detailed appendix describing the prompt-generation process and (2) add an explicit limitations paragraph acknowledging the lack of crowdsourced validation while recommending such validation for future benchmarks. revision: partial

Circularity Check

0 steps flagged

No circularity: pure empirical reporting of prompt sensitivity scores

full rationale

The paper performs a direct empirical evaluation of six embedding models on eleven datasets using fifteen task-specific prompts each, computing and comparing performance scores and rankings across these fixed inputs. No mathematical derivations, equations, fitted parameters, or self-referential definitions appear in the reported chain; the central claim that any model can reach first place follows from exhaustive enumeration of the chosen prompt set rather than from any reduction to prior outputs or self-citations. The prompt construction itself is presented as an experimental design choice without being derived from the resulting scores, keeping the analysis self-contained against the observed data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical distribution of scores across the chosen prompts rather than on new theoretical constructs, fitted parameters, or invented entities.

axioms (1)
  • domain assumption The fifteen prompts per dataset are representative of plausible user instructions.
    This assumption is required to generalize the observed sensitivity beyond the specific prompts tested.

pith-pipeline@v0.9.0 · 5680 in / 1080 out tokens · 62481 ms · 2026-05-22T06:26:26.805403+00:00 · methodology

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