Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning (2409.12618v2)

Abstract: Iterative human engagement is a common and effective means of leveraging the advanced language processing power of LLMs. Using well-structured prompts in a conversational manner, human users can effectively influence an LLM to develop more thoughtful and accurate responses. Motivated by this insight, we propose the Iteration of Thought (IoT) framework for enhancing LLM responses by generating "thought"-provoking prompts vis a vis an input query and the current iteration of an LLM's response. Unlike static or semi-static approaches, e.g. Chain of Thought (CoT) or Tree of Thoughts (ToT), IoT adapts its reasoning path dynamically, based on evolving context, and without generating alternate explorative thoughts which are ultimately discarded. The three components of the IoT framework are (1) an Inner Dialogue Agent (IDA) responsible for generating instructive, context-specific prompts; (2) an LLM Agent (LLMA) that processes these prompts to refine its responses; and (3) an iterative prompting loop that implements a conversation between the former two components. We introduce two variants of our framework: Autonomous Iteration of Thought (AIoT), where an LLM decides when to stop iterating, and Guided Iteration of Thought (GIoT), which always forces a fixed number iterations. We investigate the performance of IoT across various datasets, spanning complex reasoning tasks from the GPQA dataset, explorative problem-solving in Game of 24, puzzle solving in Mini Crosswords, and multi-hop question answering from the HotpotQA dataset. Our results show that IoT represents a viable paradigm for autonomous response refinement in LLMs, showcasing significant improvements over CoT and thereby enabling more adaptive and efficient reasoning systems that minimize human intervention.

• The paper introduces the Iteration of Thought framework employing an inner dialogue agent and an LLM agent to dynamically refine responses.
• It demonstrates accuracy improvements up to 14.11% over static methods and compares AIoT with GIoT, CoT, and IO across diverse tasks.
• The study highlights a balanced trade-off between autonomous and guided iterations, offering actionable insights for advanced AI reasoning.

Evaluation of the Iteration of Thought (IoT) Framework for Autonomous LLM Reasoning

The paper, "Iteration of Thought: Leveraging Inner Dialogue for Autonomous LLM Reasoning," explores a novel framework for enhancing the reasoning capabilities of LLMs. The authors introduce an Iteration of Thought (IoT) framework designed to generate "thought"-provoking prompts dynamically, driven by an Inner Dialogue Agent (IDA) and an LLM Agent (LLMA). This approach contrasts with static or semi-static methods like Chain of Thought (CoT) and Tree of Thoughts (ToT), which may struggle to adapt to evolving contexts.

IoT Framework Overview

The IoT framework is built on three main components:

1. Inner Dialogue Agent (IDA): Generates instructive, context-specific prompts based on the original query and current LLM responses.
2. LLM Agent (LLMA): Processes the prompts generated by IDA to refine its responses.
3. Iterative Prompting Loop: Facilitates a conversation between IDA and LLMA until a satisfactory answer is achieved or a maximum iteration count is reached.

Two variants of IoT are introduced: Autonomous Iteration of Thought (AIoT) and Guided Iteration of Thought (GIoT). AIoT relies on the LLM to decide when to stop iterating, optimizing computing resources and time. Conversely, GIoT enforces a fixed number of iterations, promoting thorough exploration but increasing computational cost.

Experimental Evaluation

GPQA Questionnaire

Using the GPQA Diamond dataset, the authors compared AIoT and GIoT against CoT and simple Input-Output (IO) methods. Results indicate that AIoT offers substantial accuracy improvements (up to 14.11%) over IO, while GIoT performs slightly better than CoT (2.62% improvement). AIoT's adaptive, autonomous reasoning mechanism effectively balances the exploration of solution spaces without falling into over-iteration or premature convergence, a risk associated with GIoT.

Explorative Problem-Solving Tasks

The experiment included tasks like Game of 24 and Mini Crosswords, which benefit from broad exploratory reasoning. Results indicate GIoT outperforms AIoT, CoT, and IO in such tasks, aligning its performance with ToT. GIoT's enforced iteration strategy ensures comprehensive exploration, highlighting its suitability for complex problem-solving scenarios involving multiple potential pathways.

Multi-Context Reasoning and Retrieval Tasks

The HotpotQA-Hard dataset tests multi-hop question answering across numerous contexts. AIoT demonstrated clear superiority over CoT, achieving F1 and ROUGE-L scores significantly higher by enabling dynamic, iterative refinement. Comparisons with the AgentLite framework show AIoT's higher F1 and EM scores, underscoring the efficacy of adaptive, autonomous reasoning for complex, multi-hop tasks.

Implications and Future Directions

The IoT framework illustrates its effectiveness in both reasoning and adaptability. By dynamically adjusting reasoning paths, it offers a robust alternative to static methods, minimizing reliance on human intervention. This trait is particularly beneficial in real-world scenarios demanding rapid, continual decision-making.

Extensions to the IoT framework include hybrid methods combining IoT with CoT, utilizing distinct LLMs for IDA and LLMA, and expanding IDA into a meta-agent with specialized sub-agents. These modifications could further enhance reasoning capabilities, support larger knowledge bases, and address hallucination risks.

Exploring specialized LLMs, fine-tuning with additional datasets, and integrating external feedback mechanisms could bolster AIoT and GIoT's performance, making IoT a potent tool for autonomous LLM reasoning and application in diverse, complex domains.

In conclusion, the IoT framework exemplifies a significant progression in autonomous LLM reasoning, merging dynamic adaptability with iterative refinement. By addressing contemporary challenges in AI reasoning, IoT not only enhances performance but also sets the stage for future advancements in large-scale, autonomous AI systems. The promising results across various tasks affirm its potential as a cornerstone for future AI research and applications.