Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | a high prompt sensitivity has been widely accepted as a core limitation of large language models . a recent study suggests that prompt senescence may be an artifact of evaluation processes . |
| Approach: | They examine whether prompt sensitivity is an inherent weakness or an artifact of evaluation . they find that heuristic evaluation methods overlook semantically correct responses . large language models have achieved remarkable success across a wide range of tasks . |
| Outcome: | The proposed model is more robust to prompt templates than previously thought . the authors show that prompt sensitivity may be an artifact of evaluation rather than a flaw . |
Similar Papers
POSIX: A Prompt Sensitivity Index For Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are sensitive to minor variations in prompts, such as spelling errors, alteration of wording or the prompt template. |
| Approach: | They propose a PrOmpt Sensitivity IndeX to measure prompt sensitivity . they use this to compare prompt sensitability of various open source LLMs . |
| Outcome: | The proposed method can measure and compare prompt sensitivity of open source LLMs. |
ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent research has neglected instances-level prompt variations and their implications on subjective evaluations. |
| Approach: | They propose a framework to evaluate and comprehend prompt sensitivity in large language models. |
| Outcome: | The proposed framework evaluates and comprehends prompt sensitivity in large language models. |
Social Bias Evaluation for Large Language Models Requires Prompt Variations (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have tried to evaluate and mitigate social biases accurately using limited prompts. |
| Approach: | They investigate the sensitivity of Large Language Models when changing prompt variations . they found that LLM rankings fluctuate across prompts for both task performance and social bias . |
| Outcome: | The results show that LLM rankings fluctuate when changing prompt variations . |
When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are sensitive to subtle, non-semantic variations in prompt phrasing and formatting. |
| Approach: | They propose to evaluate 4 methods for improving prompt robustness within a unified experimental framework. |
| Outcome: | The proposed methods are compared to 8 models from Llama, Qwen and Gemma families and are generalized against multiple types of distribution shifts. |
What Did I Do Wrong? Quantifying LLMs’ Sensitivity and Consistency to Prompt Engineering (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have significantly improved productivity in a number of routine tasks. |
| Approach: | They propose two metrics for classification tasks, namely *sensitivity* and *consistency*, which are complementary to task performance. |
| Outcome: | The proposed metrics are complementary to task performance and can be used to guide prompt engineering and obtain LLMs that balance robustness and performance. |
Understanding the Prompt Sensitivity (2026.acl-long)
Copied to clipboard
| Challenge: | Prompt sensitivity is a measure of how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt. |
| Approach: | They consider LLMs as multivariate functions and perform a first-order Taylor expansion to analyze the relationship between meaning-preserving prompts, their gradients, and log probabilities of the model’s next token. |
| Outcome: | The proposed model disperses meaning-preserving inputs, making it difficult to reduce to 0. The proposed models also dispersing prompt variants are more likely to introduce prompt sensitivity risks in LLMs. |
You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments (2024.naacl-long)
Copied to clipboard
Bangzhao Shu, Lechen Zhang, Minje Choi, Lavinia Dunagan, Lajanugen Logeswaran, Moontae Lee, Dallas Card, David Jurgens
| Challenge: | Large Language Models (LLMs) are popular for research in social sciences . currently, prompting LLMs is insufficient to accurately and reliably capture model perceptions, and we discuss potential alternatives to improve this. |
| Approach: | They construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes and a set of questions containing minor variations. |
| Outcome: | The proposed model can generate answers and negate statements in a consistent and robust manner. |
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
Copied to clipboard
Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
| Approach: | They conduct the first analysis of n-gram token-level mechanisms . they find that higher average performance is inherently associated with lower variance and greater stability. |
| Outcome: | The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants . |
Optimization before Evaluation: Evaluation with Unoptimized Prompts Can be Misleading (2025.acl-industry)
Copied to clipboard
| Challenge: | Current Large Language Model (LLM) evaluation frameworks use the same static prompt template across all models under evaluation. |
| Approach: | They investigate the effect of PO on LLM evaluations by using public academic and internal benchmarks to optimize the prompt for each model. |
| Outcome: | The proposed frameworks use the same static prompt template across all models under evaluation, but have some drawbacks. |
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are widely used in decision-making across diverse domains. |
| Approach: | They propose a prompt-response concept model that explains the relationship between the amount of task-relevant information provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty. |
| Outcome: | The proposed model shows that the amount of information provided in the prompt influences the LLM-generated response uncertainty. |