Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning

Original Paper: https://arxiv.org/abs/2409.12618

By: Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas

Abstract:

Iterative human engagement is a common and effective means of leveraging the advanced language processing power of large language models (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 (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.

Summary Notes

Figure: Illustration of different prompting strategies for enhancing LLM reasoning capabilities. The Input-Output (IO) method uses a direct input-output approach with no intermediate reasoning. Chain-of-Thought (CoT) (Wei et al., 2022) prompts introduce a single, linear reasoning path, while Tree-of-Thought (ToT) (Yao et al., 2024) methods expand this by exploring multiple reasoning paths in parallel. The proposed Iteration-of-Thought (IoT) (This work) framework introduces an Inner Dialogue Agent (IDA) to dynamically adjust reasoning paths, enabling adaptive cross-path exploration to enhance response accuracy.

Introduction

In the fast-evolving world of artificial intelligence, Large Language Models (LLMs) like GPT-3 and its successors have transformed natural language processing. However, a common observation is that the performance of these models can significantly improve with iterative prompting. The newly proposed framework, Iteration of Thought (IoT), takes this concept further by dynamically refining the reasoning paths of LLMs, resulting in more precise and context-aware responses. This blog post delves into the mechanics, advantages, and real-world applications of the IoT framework, which promises to elevate the capabilities of LLMs.

Key Methodologies: The IoT Framework

The Iteration of Thought (IoT) framework introduces a novel approach that leverages an Inner Dialogue Agent (IDA) to guide the reasoning process of LLMs iteratively. The IoT framework consists of three core components:

1. Inner Dialogue Agent (IDA): This component generates context-sensitive prompts based on the initial user query and the LLM’s previous responses. It acts as a guide, dynamically adjusting the prompts to lead the LLM towards more accurate answers.
2. LLM Agent (LLMA): The LLMA is responsible for processing the IDA’s prompts using its internal knowledge base to refine responses. It identifies areas of uncertainty, providing feedback for the IDA to adjust prompts accordingly.
3. Iterative Prompting Loop: This loop facilitates a back-and-forth interaction between the IDA and LLMA. The process continues until a satisfactory answer is found or a maximum iteration count is reached, allowing the framework to explore complex reasoning paths efficiently.

Main Findings: Improved Reasoning and Response Accuracy

The IoT framework was tested across various datasets, showcasing its ability to enhance LLM performance significantly. Key findings include:

• The IoT framework, particularly its Autonomous Iteration of Thought (AIoT) variant, outperformed traditional methods like Chain of Thought (CoT) by a substantial margin, achieving a 14.11% improvement in accuracy on the GPQA Diamond dataset.
• The Guided Iteration of Thought (GIoT) variant, which mandates a fixed number of iterations, demonstrated superior performance in tasks requiring extensive exploration, such as the Game of 24 and Mini Crosswords, by ensuring comprehensive reasoning path coverage.
• In multi-context reasoning tasks like HotpotQA, AIoT achieved higher F1 and Exact Match scores than contemporary frameworks, affirming its robustness in synthesizing information across disjoint contexts.

Implications and Real-World Applications

The implications of the IoT framework are vast, with potential applications spanning various domains:

• Autonomous Systems: IoT’s ability to function independently, without human intervention, makes it ideal for applications requiring rapid and continuous decision-making, such as autonomous vehicles or real-time data analysis.
• Enhanced Human-LLM Interaction: By improving the accuracy and coherence of LLM responses, IoT can bolster applications in customer support, content generation, and personalized assistant technologies.
• Educational Tools: IoT's iterative refinement process can be leveraged in educational platforms to provide students with more precise and contextually relevant information, enhancing learning experiences.

Conclusion

The Iteration of Thought framework represents a significant advancement in the realm of LLM reasoning. By employing an adaptive, iterative approach, IoT enhances the ability of models to navigate complex reasoning tasks, providing more accurate and context-aware responses. As AI continues to evolve, frameworks like IoT will be crucial in unlocking the full potential of language models, driving innovation across various industries. The future of AI is promising, with IoT paving the way for more intelligent and autonomous language processing systems.