Summary of Iteration Of Thought: Leveraging Inner Dialogue For Autonomous Large Language Model Reasoning, by Santosh Kumar Radha et al.
Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
by Santosh Kumar Radha, Yasamin Nouri Jelyani, Ara Ghukasyan, Oktay Goktas
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Iteration of Thought (IoT) framework enhances the responses of large language models (LLMs) by generating thought-provoking prompts. The IoT framework consists of three components: an Inner Dialogue Agent, an LLM Agent, and an iterative prompting loop that implements a conversation between them. Two variants are introduced: Autonomous Iteration of Thought (AIoT), where the LLM decides when to stop iterating, and Guided Iteration of Thought (GIoT), which always forces a fixed number of iterations. The framework is tested on various datasets, including complex reasoning tasks from GPQA, explorative problem-solving in Game of 24, puzzle solving in Mini Crosswords, and multi-hop question answering from HotpotQA. Results show that IoT improves response refinement in LLMs, enabling more adaptive and efficient reasoning systems with minimal human intervention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IoT is a new way to make large language models better at giving thoughtful answers. It works by having humans give the model special prompts to help it think more clearly. The model then uses these prompts to give its answers. This process keeps going until the model has a good answer. There are two ways to use IoT: one where the model decides when to stop, and another where a person chooses how many times the model will try. Researchers tested this new way of making models better on different tasks, like puzzles and answering tricky questions. The results show that IoT makes models more accurate and efficient at giving good answers. |
Keywords
» Artificial intelligence » Prompting » Question answering
