POSIX: A Prompt Sensitivity Index For Large Language Models

• URL: http://arxiv.org/abs/2410.02185v2
• Date: Fri, 4 Oct 2024 07:00:03 GMT
• Title: POSIX: A Prompt Sensitivity Index For Large Language Models
• Authors: Anwoy Chatterjee, H S V N S Kowndinya Renduchintala, Sumit Bhatia, Tanmoy Chakraborty,
• Abstract summary: Large Language Models (LLMs) are surprisingly sensitive to minor variations in prompts. POSIX is a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity.
• Score: 22.288479270814484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their remarkable capabilities, Large Language Models (LLMs) are found to be surprisingly sensitive to minor variations in prompts, often generating significantly divergent outputs in response to minor variations in the prompts, such as spelling errors, alteration of wording or the prompt template. However, while assessing the quality of an LLM, the focus often tends to be solely on its performance on downstream tasks, while very little to no attention is paid to prompt sensitivity. To fill this gap, we propose POSIX - a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity, thereby offering a more comprehensive evaluation of LLM performance. The key idea behind POSIX is to capture the relative change in loglikelihood of a given response upon replacing the corresponding prompt with a different intent-preserving prompt. We provide thorough empirical evidence demonstrating the efficacy of POSIX in capturing prompt sensitivity and subsequently use it to measure and thereby compare prompt sensitivity of various open-source LLMs. We find that merely increasing the parameter count or instruction tuning does not necessarily reduce prompt sensitivity whereas adding some few-shot exemplars, even just one, almost always leads to significant decrease in prompt sensitivity. We also find that alterations to prompt template lead to the highest sensitivity in the case of MCQ type tasks, whereas paraphrasing results in the highest sensitivity in open-ended generation tasks. The code for reproducing our results is open-sourced at https://github.com/kowndinya-renduchintala/POSIX.

Related papers

• ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs [72.13489820420726]
  ProSA is a framework designed to evaluate and comprehend prompt sensitivity in large language models. Our study uncovers that prompt sensitivity fluctuates across datasets and models, with larger models exhibiting enhanced robustness.
  arXiv  Detail & Related papers  (2024-10-16T09:38:13Z)
• What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering [8.019873464066308]
  We introduce two metrics for classification tasks, namely sensitivity and consistency. sensitivity measures changes of predictions across rephrasings of the prompt. Instead, consistency measures how predictions vary across rephrasings for elements of the same class.
  arXiv  Detail & Related papers  (2024-06-18T06:59:24Z)
• On the Worst Prompt Performance of Large Language Models [93.13542053835542]
  Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts. We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries. Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
  arXiv  Detail & Related papers  (2024-06-08T13:40:38Z)
• GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model [66.86722460851968]
  We propose a gold label-agnostic prompt evaluation (GLaPE) to alleviate dependence on gold labels. We show that GLaPE provides reliable evaluations with accuracy, even in the absence of gold labels. On six popular reasoning tasks, our GLaPE-based prompt optimization yields effective prompts comparable to accuracy-based ones.
  arXiv  Detail & Related papers  (2024-02-04T08:57:54Z)
• How are Prompts Different in Terms of Sensitivity? [50.67313477651395]
  We present a comprehensive prompt analysis based on the sensitivity of a function. We use gradient-based saliency scores to empirically demonstrate how different prompts affect the relevance of input tokens to the output. We introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding.
  arXiv  Detail & Related papers  (2023-11-13T10:52:01Z)
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting [68.19544657508509]
  Large language models (LLMs) are adopted as a fundamental component of language technologies. We find that several widely used open-source LLMs are extremely sensitive to subtle changes in prompt format in few-shot settings. We propose an algorithm that rapidly evaluates a sampled set of plausible prompt formats for a given task, and reports the interval of expected performance without accessing model weights.
  arXiv  Detail & Related papers  (2023-10-17T15:03:30Z)
• Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions [10.621564997491808]
  Chain-of-thought (CoT) prompting has been shown to empirically improve the accuracy of large language models. We investigate whether CoT prompting affects the relative importances they assign to particular input tokens. Our results indicate that while CoT prompting does not increase the magnitude of saliency scores attributed to semantically relevant tokens in the prompt, it increases the robustness of saliency scores to question perturbations and variations in model output.
  arXiv  Detail & Related papers  (2023-07-25T08:51:30Z)
• Demystifying Prompts in Language Models via Perplexity Estimation [109.59105230163041]
  Performance of a prompt is coupled with the extent to which the model is familiar with the language it contains. We show that the lower the perplexity of the prompt is, the better the prompt is able to perform the task.
  arXiv  Detail & Related papers  (2022-12-08T02:21:47Z)
• Sensitivity as a Complexity Measure for Sequence Classification Tasks [24.246784593571626]
  We argue that standard sequence classification methods are biased towards learning low-sensitivity functions, so that tasks requiring high sensitivity are more difficult. We estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher on challenging tasks collected in GLUE than on simple text classification tasks.
  arXiv  Detail & Related papers  (2021-04-21T03:56:59Z)

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.