Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach

• URL: http://arxiv.org/abs/2503.13208v3
• Date: Sun, 13 Apr 2025 12:38:06 GMT
• Title: Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
• Authors: Sinan Fan, Liang Xie, Chen Shen, Ge Teng, Xiaosong Yuan, Xiaofeng Zhang, Chenxi Huang, Wenxiao Wang, Xiaofei He, Jieping Ye,
• Abstract summary: We propose Dynamic Prompt Corruption (DPC) to take better advantage of soft prompts in complex reasoning tasks.<n>First, Dynamic Trigger measures the impact of soft prompts, identifying whether beneficial or detrimental.<n>Then, Dynamic Corruption mitigates the negative effects of soft prompts by selectively masking key tokens that interfere with the reasoning process.
• Score: 33.331269103351815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our investigation reveals that PT provides limited improvement and may even degrade the primitive performance of LLMs on complex reasoning tasks. Such a phenomenon suggests that soft prompts can positively impact certain instances while negatively affecting others, particularly during the later phases of reasoning. To address these challenges, We first identify an information accumulation within the soft prompts. Through detailed analysis, we demonstrate that this phenomenon is often accompanied by erroneous information flow patterns in the deeper layers of the model, which ultimately lead to incorrect reasoning outcomes. we propose a novel method called Dynamic Prompt Corruption (DPC) to take better advantage of soft prompts in complex reasoning tasks, which dynamically adjusts the influence of soft prompts based on their impact on the reasoning process. Specifically, DPC consists of two stages: Dynamic Trigger and Dynamic Corruption. First, Dynamic Trigger measures the impact of soft prompts, identifying whether beneficial or detrimental. Then, Dynamic Corruption mitigates the negative effects of soft prompts by selectively masking key tokens that interfere with the reasoning process. We validate the proposed approach through extensive experiments on various LLMs and reasoning tasks, including GSM8K, MATH, and AQuA. Experimental results demonstrate that DPC can consistently enhance the performance of PT, achieving 4%-8% accuracy gains compared to vanilla prompt tuning, highlighting the effectiveness of our approach and its potential to enhance complex reasoning in LLMs.

Related papers

• PAFT: Prompt-Agnostic Fine-Tuning [11.834072667345957]
  We propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach that adjusts dynamically prompts during fine-tuning.<n>PAFT operates in two stages: First, a diverse set of meaningful, synthetic candidate prompts is constructed.<n>Second, during fine-tuning, prompts are randomly sampled from this set to create dynamic training inputs.
  arXiv  Detail & Related papers  (2025-02-18T13:46:47Z)
• Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product [8.014705094248589]
  Low- parameters prompt tuning method outperforms state-of-the-art PT-based and LoRA-based methods in performance and efficiency.<n>Experiments across six architectures and eight datasets demonstrate that LAMP outperforms state-of-the-art PT-based and LoRA-based methods in performance and efficiency.
  arXiv  Detail & Related papers  (2025-02-16T05:50:12Z)
• Benchmarking Prompt Sensitivity in Large Language Models [13.986971540998258]
  Large language Models (LLMs) are highly sensitive to variations in prompt formulation.<n>This paper introduces a new task, Prompt Sensitivity Prediction, and a dataset designed to investigate the effects of slight prompt variations on LLM performance.
  arXiv  Detail & Related papers  (2025-02-09T23:01:03Z)
• The Order Effect: Investigating Prompt Sensitivity in Closed-Source LLMs [19.798249518847694]
  This paper investigates the extent of order sensitivity in large language models (LLMs) Our results show that input order significantly affects performance across tasks, with shuffled inputs leading to measurable declines in output accuracy. Few-shot prompting demonstrates mixed effectiveness and offers partial mitigation, however, fails to fully resolve the problem.
  arXiv  Detail & Related papers  (2025-02-06T15:14:02Z)
• Investigating the Robustness of Deductive Reasoning with Large Language Models [7.494617747914778]
  Large Language Models (LLMs) have been shown to achieve impressive results for many reasoning-based Natural Language Processing (NLP) tasks. It remains unclear to which extent LLMs, in both informal and autoformalisation methods, are robust on logical deduction tasks.
  arXiv  Detail & Related papers  (2025-02-04T17:16:51Z)
• Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
  Large Language Models (LLMs) are increasingly recognized for their practical applications. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs. By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• 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)
• 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)
• Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications [63.29358103217275]
  Large Language Models (LLMs) often leads to reduced performance, especially for knowledge-intensive tasks. We propose two conjectures on the nature of the damage: one is certain knowledge being forgotten (or erased) after compression. We introduce a variant called Inference-time Dynamic Prompting (IDP) that can effectively increase prompt diversity without incurring any inference overhead.
  arXiv  Detail & Related papers  (2023-10-02T03:12:06Z)
• Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models [81.01397924280612]
  Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains.
  arXiv  Detail & Related papers  (2023-04-23T13:54:39Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)

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.