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arxiv: 2505.13360 · v3 · submitted 2025-05-19 · 💻 cs.CL · cs.SE

What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts

Chenyang Yang , Yike Shi , Qianou Ma , Michael Xieyang Liu , Christian K\"astner , Tongshuang Wu This is my paper

Pith reviewed 2026-05-22 13:58 UTC · model grok-4.3

classification 💻 cs.CL cs.SE
keywords prompt underspecificationLLM reliabilityprompt optimizationrequirements discoverymodel regressioninstruction followingLLM applicationsprompt stability
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The pith

Under-specified prompts make LLMs twice as likely to regress across model or prompt changes, yet requirements-aware optimization lifts results by 4.8 percent on average.

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

The paper analyzes how prompts that leave key requirements unspecified affect large language model behavior. Models infer the missing details by default in roughly 41 percent of cases, but this inference proves unstable. Under-specified prompts regress twice as often when models or prompts are altered, and accuracy can fall more than 20 percent. Simply writing out every requirement does not fix the issue reliably because models follow instructions inconsistently and requirements sometimes conflict. Standard prompt optimizers also fail to help much, while the authors' requirements-aware methods deliver steadier gains.

Core claim

LLMs often infer unspecified requirements in prompts by default in 41.1 percent of cases, but this behavior is fragile because under-specified prompts are twice as likely to regress across model or prompt changes, with accuracy drops exceeding 20 percent in some instances. Simply specifying all requirements does not consistently improve outcomes due to limited instruction-following abilities and potential conflicts. Standard prompt optimizers offer little help, whereas the proposed requirements-aware prompt optimization mechanisms achieve an average performance improvement of 4.8 percent over baselines.

What carries the argument

Requirements-aware prompt optimization mechanisms that perform proactive requirements discovery, evaluation, and ongoing monitoring to reduce the effects of underspecification.

If this is right

  • Under-specified prompts are twice as likely to regress when models or prompts change.
  • Accuracy drops exceeding 20 percent can occur from this instability.
  • Explicitly stating every requirement does not reliably improve performance due to inconsistent instruction following and requirement conflicts.
  • Standard prompt optimization techniques deliver little benefit against underspecification.
  • Requirements-aware optimization mechanisms improve average performance by 4.8 percent.

Where Pith is reading between the lines

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

  • Developers could add routine audits for missing requirements during prompt creation to reduce later regressions.
  • Automated tools that flag and suggest missing requirements might become practical for production systems.
  • Ongoing performance monitoring in deployed applications could catch instability before it affects users.
  • Similar fragility patterns may appear in multi-turn or agent-based LLM setups and warrant separate checks.

Load-bearing premise

The evaluation tasks and metrics used accurately capture the real effects of underspecification in typical LLM application scenarios.

What would settle it

A replication on a wider range of practical tasks showing that under-specified prompts do not regress at twice the rate or that the new optimization methods lose their reported gains.

Figures

Figures reproduced from arXiv: 2505.13360 by Chenyang Yang, Christian K\"astner, Michael Xieyang Liu, Qianou Ma, Tongshuang Wu, Yike Shi.

Figure 1
Figure 1. Figure 1: Developers often underspecify prompts and miss user-important requirements, leading to divergent [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cumulative distribution of accuracy drop [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: LLMs’ average accuracy on specified re￾quirements drops with more requirements specified in the prompt, especially for smaller models like Llama-3.3-70B-Instruct. ified requirements (with different default behav￾iors). Regressions of unspecified requirements are both more frequent and far harder to detect. This makes it necessary to regularly evaluate and moni￾tor known unspecified requirements (Section 5)… view at source ↗
Figure 5
Figure 5. Figure 5: We gather 60 requirements for our analysis. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sample annotator instruction during require [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompts for requirement elicitation - Brain [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompts for requirement elicitation - Error [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompts for requirement evaluation: Plan [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: We use a cyclic design to generate prompts. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: To discover an unspecified requirement reli [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparing LLM+Prompts performances on specified requirements vs. unspecified requirements, we found that, overall, LLM+Prompts perform worse and diverge more for unspecified requirements. This is statistically significant even if we consider all other factors and explains a large portion of the variances observed ( [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The histogram of average requirement accu [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Unoptimized prompts generated from the prompt template (acc=75.4%). [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: COPRO-optimized prompts (acc=86.7%). We found COPRO-optimized prompts tend to reorder [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Unoptimized prompt from Cursor Community (acc=44.1%). [PITH_FULL_IMAGE:figures/full_fig_p029_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Bayesian-optimized prompts (acc=47.4%). We found Bayesian-optimized prompts use much fewer [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗
read the original abstract

Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing that while LLMs can often infer unspecified requirements by default (41.1%), such behavior is fragile: Under-specified prompts are 2x as likely to regress across model or prompt changes, sometimes with accuracy drops exceeding 20%. This instability makes it difficult to reliably build LLM applications. Moreover, simply specifying all requirements does not consistently help, as models have limited instruction-following ability and requirements can conflict. Standard prompt optimizers likewise provide little benefit. To address these issues, we propose requirements-aware prompt optimization mechanisms that improve performance by 4.8% on average over baselines. We further advocate for a systematic process of proactive requirements discovery, evaluation, and monitoring to better manage prompt underspecification in practice.

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

2 major / 2 minor

Summary. The paper analyzes prompt underspecification in LLMs, reporting that models default-infer unspecified requirements in 41.1% of cases. It finds that underspecified prompts are 2x as likely to regress across model or prompt changes (with accuracy drops sometimes exceeding 20%), that fully specifying requirements does not consistently help due to limited instruction-following and conflicts, and that standard prompt optimizers provide little benefit. The authors propose requirements-aware prompt optimization mechanisms yielding 4.8% average improvement over baselines and advocate a systematic process of proactive requirements discovery, evaluation, and monitoring.

Significance. If the quantitative results on regression likelihood and optimization gains hold under scrutiny, the work would be significant for highlighting a practical fragility in LLM prompting and for offering concrete mitigation strategies. The empirical focus on observable behaviors rather than self-referential derivations is a strength, as is the proposal of requirements-aware mechanisms that could inform more reliable LLM application design.

major comments (2)
  1. [Methods] Methods section: The headline claims of 2x regression likelihood and 4.8% optimization gains rest on specific task collections and a particular definition of regression across model/prompt changes. The paper must demonstrate that these tasks are representative of real-world scenarios and that the regression metric is robust to reasonable variations in threshold or baseline; otherwise the factor-of-two and reported gains risk being artifacts of experimental design rather than general properties of underspecification.
  2. [§4] §4 (Evaluation): The abstract reports concrete figures (41.1% default inference, >20% drops, 4.8% gain) but the evaluation must include explicit controls, statistical tests, and ablation on whether the chosen metrics isolate underspecification effects versus other prompt sensitivities; without these the central stability claims remain difficult to verify.
minor comments (2)
  1. [§5] Clarify the exact operational definition of 'requirements-aware prompt optimization' and how it differs from standard optimizers in the proposed mechanisms.
  2. [Discussion] Add discussion of potential conflicts between requirements and how the systematic process handles them in practice.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed the major comments and provide point-by-point responses below, outlining how we will strengthen the paper through targeted revisions.

read point-by-point responses

  1. Referee: [Methods] Methods section: The headline claims of 2x regression likelihood and 4.8% optimization gains rest on specific task collections and a particular definition of regression across model/prompt changes. The paper must demonstrate that these tasks are representative of real-world scenarios and that the regression metric is robust to reasonable variations in threshold or baseline; otherwise the factor-of-two and reported gains risk being artifacts of experimental design rather than general properties of underspecification.

    Authors: Our task collection draws from established benchmarks spanning classification, generation, and reasoning to capture typical LLM application patterns. We agree that explicit robustness checks are valuable and have added sensitivity analyses in the revised Methods section: varying the regression threshold by ±5% and using alternative baselines such as mean performance across models. These confirm the approximately 2x regression likelihood holds. We have also expanded the task selection rationale with a limitations discussion on representativeness, noting that while our set reflects common prompt-engineering scenarios, broader real-world coverage would require future multi-domain studies. revision: yes

  2. Referee: [§4] §4 (Evaluation): The abstract reports concrete figures (41.1% default inference, >20% drops, 4.8% gain) but the evaluation must include explicit controls, statistical tests, and ablation on whether the chosen metrics isolate underspecification effects versus other prompt sensitivities; without these the central stability claims remain difficult to verify.

    Authors: We concur that stronger statistical grounding and isolation of effects will improve verifiability. The revised §4 now includes paired t-tests and bootstrap confidence intervals for the reported figures (41.1% default inference, accuracy drops, and 4.8% gain). We added control experiments contrasting underspecification with other prompt perturbations (e.g., lexical noise) and an ablation removing individual components of the requirements-aware optimizer. These results, with full tables in the appendix, support that the stability claims are attributable to underspecification rather than generic prompt sensitivity. revision: yes

Circularity Check

0 steps flagged

No circularity detected in empirical LLM prompt analysis

full rationale

The paper reports direct experimental measurements of LLM behavior under underspecified prompts, including observed rates (41.1% default inference) and comparative statistics (2x regression likelihood, 4.8% optimization gains). These quantities are computed from task evaluations rather than derived via equations, fitted parameters renamed as predictions, or self-referential definitions. No load-bearing self-citations, uniqueness theorems, or ansatzes appear in the abstract or described methods. The work is self-contained as an empirical investigation against external benchmarks, with results tied to observable model outputs instead of reducing to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Claims rest on the domain assumption that underspecification can be isolated and measured in LLM outputs and that the proposed optimization targets the root cause rather than correlated factors.

axioms (1)
  • domain assumption LLMs can infer unspecified requirements from prompts in measurable ways
    Paper quantifies this at 41.1% and treats it as a baseline behavior for comparison.

pith-pipeline@v0.9.0 · 5693 in / 1190 out tokens · 56463 ms · 2026-05-22T13:58:38.862338+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

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    **Conciseness**: Ensure the explanation does not exceed 500 words, maintaining focus and clarity. Figure 16: COPRO-optimized prompts (acc=86.7%). We found COPRO-optimized prompts tend to reorder requirements in a more logical structure, merge related requirements together, and sometimes drop requirements. D Additional tasks setups and results Tasks and da...

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