Instance-adaptive Zero-shot Chain-of-Thought Prompting

• URL: http://arxiv.org/abs/2409.20441v3
• Date: Thu, 31 Oct 2024 03:34:33 GMT
• Title: Instance-adaptive Zero-shot Chain-of-Thought Prompting
• Authors: Xiaosong Yuan, Chen Shen, Shaotian Yan, Xiaofeng Zhang, Liang Xie, Wenxiao Wang, Renchu Guan, Ying Wang, Jieping Ye,
• Abstract summary: Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts.
• Score: 32.700073951068575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e.g., GSM8K, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism.

Related papers

• Understanding Chain-of-Thought in LLMs through Information Theory [16.78730663293352]
  We formalize Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) through an information-theoretic lens. Specifically, our framework quantifies the information gain' at each reasoning step, enabling the identification of failure modes. We demonstrate the efficacy of our approach through extensive experiments on toy and GSM-8K data, where it significantly outperforms existing outcome-based methods.
  arXiv  Detail & Related papers  (2024-11-18T19:14:36Z)
• Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning [52.83539473110143]
  We introduce a novel structure-oriented analysis method to help Large Language Models (LLMs) better understand a question. To further improve the reliability in complex question-answering tasks, we propose a multi-agent reasoning system, Structure-oriented Autonomous Reasoning Agents (SARA) Extensive experiments verify the effectiveness of the proposed reasoning system. Surprisingly, in some cases, the system even surpasses few-shot methods.
  arXiv  Detail & Related papers  (2024-10-18T05:30:33Z)
• Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension [9.67774998354062]
  Previous research has primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. We propose a Premise-Oriented Data Augmentation (PODA) framework to generate CoT rationales including analyses for both correct and incorrect options. We also introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
  arXiv  Detail & Related papers  (2024-09-22T15:44:43Z)
• To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning [55.52872152909785]
  Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs) We show that CoT gives strong performance benefits primarily on tasks involving math or logic, with much smaller gains on other types of tasks.
  arXiv  Detail & Related papers  (2024-09-18T17:55:00Z)
• Break the Chain: Large Language Models Can be Shortcut Reasoners [18.047917626825548]
  Chain-of-Thought (CoT) reasoning utilize complex modules but are hampered by high token consumption, limited applicability, and challenges in thinking. This paper conducts a critical evaluation of CoT prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks. We propose the integration of human-likes and shortcuts into language models (LMs) through "break the chain" strategies.
  arXiv  Detail & Related papers  (2024-06-04T14:02:53Z)
• Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models [11.967815199202203]
  Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks by applying zero-shot Chain-of-Thought (CoT) prompting. Existing zero-shot CoT prompting methods that employ identical CoT prompting across all task instances may not be optimal. We introduce a novel zero-shot prompting method that leverages evolutionary algorithms to generate diverse promptings for LLMs dynamically.
  arXiv  Detail & Related papers  (2024-02-08T03:17:38Z)
• LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning [61.7853049843921]
  Chain-of-thought (CoT) prompting is a popular in-context learning approach for large language models (LLMs) This paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales.
  arXiv  Detail & Related papers  (2023-12-07T20:36:10Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• Boosting Language Models Reasoning with Chain-of-Knowledge Prompting [18.326858925174605]
  Chain-of-Knowledge (CoK) prompting aims at eliciting explicit pieces of knowledge evidence in the form of structure triple. Benefiting from CoK, we additionally introduce a F2-Verification method to estimate the reliability of the reasoning chains. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.
  arXiv  Detail & Related papers  (2023-06-10T12:42:36Z)
• SatLM: Satisfiability-Aided Language Models Using Declarative Prompting [68.40726892904286]
  We propose a new satisfiability-aided language modeling (SatLM) approach for improving the reasoning capabilities of large language models (LLMs) We use an LLM to generate a declarative task specification rather than an imperative program and leverage an off-the-shelf automated theorem prover to derive the final answer. We evaluate SATLM on 8 different datasets and show that it consistently outperforms program-aided LMs in the imperative paradigm.
  arXiv  Detail & Related papers  (2023-05-16T17:55:51Z)
• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters [82.84696222087396]
  Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs) We show that CoT reasoning is possible even with invalid demonstrations.
  arXiv  Detail & Related papers  (2022-12-20T05:20:54Z)

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.