Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models

• URL: http://arxiv.org/abs/2412.16533v1
• Date: Sat, 21 Dec 2024 08:19:42 GMT
• Title: Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models
• Authors: Chao-Chi Chen, Chin-Yuan Yeh, Hsi-Wen Chen, De-Nian Yang, Ming-Syan Chen,
• Abstract summary: kNoT is a prompt scheme that advances the capabilities of large language models (LLMs) beyond existing paradigms like Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT)
• Score: 19.379144918877255
• License:
• Abstract: We introduce Knowledgeable Network of Thoughts (kNoT): a prompt scheme that advances the capabilities of large language models (LLMs) beyond existing paradigms like Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT). The key innovation of kNoT is the LLM Workflow Template (LWT), which allows for an executable plan to be specified by LLMs for LLMs. LWT allows these plans to be arbitrary networks, where single-step LLM operations are nodes, and edges correspond to message passing between these steps. Furthermore, LWT supports selection of individual elements through indexing, facilitating kNoT to produce intricate plans where each LLM operation can be limited to elementary operations, greatly enhancing reliability over extended task sequences. We demonstrate that kNoT significantly outperforms the state of the art on six use cases, while reducing the need for extensive prompt engineering. For instance, kNoT finds 92% accuracy for sorting 32 numbers over 12% and 31% for ToT and GoT, while utilizing up to 84.4% and 87.3% less task-specific prompts, respectively.

Related papers

• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data [73.29220562541204]
  We consider harnessing the amazing power of language models (LLMs) to solve our task. We develop a TAT-LLM language model by fine-tuning LLaMA 2 with the training data generated automatically from existing expert-annotated datasets.
  arXiv  Detail & Related papers  (2024-01-24T04:28:50Z)
• A Prompt Learning Framework for Source Code Summarization [19.24919436211323]
  This paper proposes an effective prompt learning framework for code summarization called PromptCS. PromptCS trains a prompt agent that can generate continuous prompts to unleash the potential for large language models in code summarization.
  arXiv  Detail & Related papers  (2023-12-26T14:37:55Z)
• ADaPT: As-Needed Decomposition and Planning with Language Models [131.063805299796]
  We introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT) ADaPT explicitly plans and decomposes complex sub-tasks as-needed, when the Large Language Models is unable to execute them. Our results demonstrate that ADaPT substantially outperforms established strong baselines.
  arXiv  Detail & Related papers  (2023-11-08T17:59:15Z)
• Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding [25.36416774024584]
  Chain-of-Thought-Thought (CoT) is a technique that guides Large Language Models (LLMs) into multi-step reasoning through intermediate steps in natural language form. We propose Chain-of-Thought-Thought (CoTT) as a two-step reasoning framework based on prompt tuning.
  arXiv  Detail & Related papers  (2023-10-18T05:39:20Z)
• Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models [47.210211555783836]
  We propose a benchmark named Natural Language Planning and Action (Natala) composed of a set of novel tasks. We find that current popular LLMs such as ChatGPT still lack abilities in complex planning. We propose a novel method called CoS (Chain-of-Symbol Prompting) that represents the complex environments with condensed symbolic spatial representations.
  arXiv  Detail & Related papers  (2023-05-17T15:07:50Z)
• 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)
• Large Language Models are Zero-Shot Reasoners [28.6899375595088]
  Chain of thought (CoT) prompting is a technique for eliciting complex multi-step reasoning through step-by-step answer examples. We show that LLMs are decent zero-shot reasoners by simply adding Let's think step by step'' before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances.
  arXiv  Detail & Related papers  (2022-05-24T09:22:26Z)

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.