DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models

• URL: http://arxiv.org/abs/2407.01009v1
• Date: Mon, 1 Jul 2024 06:45:13 GMT
• Title: DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
• Authors: Jiabao Pan, Yan Zhang, Chen Zhang, Zuozhu Liu, Hongwei Wang, Haizhou Li,
• Abstract summary: Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via Chains-of-Thought prompting. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex.
• Score: 42.95876831743256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast COT approach often encounters limitations in dealing with complicated problems, while a thorough method, which considers multiple reasoning pathways and verifies each step carefully, results in slower inference. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods, thereby optimizing both efficiency and effectiveness. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex and for which it has low confidence in immediate solutions as well as requiring more reasoning paths to verify. Experiments on five popular reasoning benchmarks demonstrated the superiority of the DynaThink over baselines.

Related papers

• Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance [33.16322104912836]
  Large language models' (LLMs) reasoning is largely due to the chain-of-thought (CoT) approaches. LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions. Human beings are naturally cognitive misers and will prompt language models to give rather short responses.
  arXiv  Detail & Related papers  (2025-04-13T14:12:14Z)
• Fast-Slow-Thinking: Complex Task Solving with Large Language Models [49.98959729052245]
  This paper introduces a new task decomposition method termed Fast-Slow-Thinking'' (FST) In FT, LLMs are prompted to remove the constraints of the original task, therefore simplifying it to a general and concise one. In ST, we recall the constraints removed in FT, so that LLMs can improve the answer generated in FT to meet the requirements of the original task.
  arXiv  Detail & Related papers  (2025-04-11T16:57:36Z)
• SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs [48.28847964704554]
  Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks. We propose a novel approach for continuous-space reasoning that does not require modifying the underlying LLM.
  arXiv  Detail & Related papers  (2025-02-17T18:52:29Z)
• Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation [68.58373854950294]
  We focus on causal reasoning and address the task of establishing causal relationships based on correlation information. We introduce a prompting strategy for this problem that breaks the original task into fixed subquestions. We evaluate our approach on an existing causal benchmark, Corr2Cause.
  arXiv  Detail & Related papers  (2024-12-18T15:32:27Z)
• Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning [40.069109287947875]
  We propose a novel reasoning framework called Forest-of-Thought (FoT) FoT integrates multiple reasoning trees to leverage collective decision-making for solving complex logical problems. FoT employs sparse activation strategies to select the most relevant reasoning paths, improving both efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-12-12T09:01:18Z)
• HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows [33.035088506211096]
  We propose a novel framework HDFlow for complex reasoning with large language models (LLMs) Our approach consists of two key components: 1) a new approach for slow, deliberate reasoning called Dynamic, which automatically decomposes complex problems into more manageable sub-tasks; and 2) Hybrid Thinking, a general framework that dynamically combines fast and slow thinking based on problem complexity. Experiments on four reasoning benchmark demonstrate that our slow thinking with dynamic datasets significantly outperforms Chain-of-Thought, and hybrid thinking achieves the highest accuracy while providing an effective balance between computational efficiency and performance.
  arXiv  Detail & Related papers  (2024-09-25T23:52:17Z)
• Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning [0.0]
  Iterative human engagement is a common and effective means of leveraging the advanced language processing power of large language models (LLMs) We propose the Iteration of Thought (IoT) framework for enhancing LLM responses by generating "thought"-provoking prompts. Unlike static or semi-static approaches, IoT adapts its reasoning path dynamically, based on evolving context.
  arXiv  Detail & Related papers  (2024-09-19T09:44:17Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
  Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
  arXiv  Detail & Related papers  (2024-05-21T17:12:19Z)
• Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models [62.96551299003463]
  We propose textbftextitThought Propagation (TP) to enhance the complex reasoning ability of Large Language Models. TP first prompts LLMs to propose and solve a set of analogous problems that are related to the input one. TP reuses the results of analogous problems to directly yield a new solution or derive a knowledge-intensive plan for execution to amend the initial solution obtained from scratch.
  arXiv  Detail & Related papers  (2023-10-06T01:40:09Z)
• Concise and Organized Perception Facilitates Reasoning in Large Language Models [32.71672086718057]
  We show that large language models (LLMs) exhibit failure patterns akin to human-like cognitive biases when dealing with disordered and irrelevant content in reasoning tasks. We propose a novel reasoning approach named Concise and Organized Perception (COP) COP carefully analyzes the given statements to identify the most pertinent information while eliminating redundancy efficiently.
  arXiv  Detail & Related papers  (2023-10-05T04:47:49Z)
• Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning [16.495754104540605]
  Large language models (LLMs) can generate code-like plans for complex inference tasks such as visual reasoning. We propose a hierarchical plan-searching algorithm that integrates the one-stop reasoning (fast) and the Tree-of-thought (slow)
  arXiv  Detail & Related papers  (2023-08-18T16:21:40Z)
• 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)
• Active Prompting with Chain-of-Thought for Large Language Models [26.5029080638055]
  This paper proposes a new method, Active-Prompt, to adapt large language models to different tasks. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty. Experimental results demonstrate the superiority of our proposed method, achieving state-of-the-art on eight complex reasoning tasks.
  arXiv  Detail & Related papers  (2023-02-23T18:58:59Z)

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.