OlaGPT: Enhancing Language Models with Human-Like Problem-Solving Abilities

Introducing OlaGPT, a groundbreaking framework designed to elevate the problem-solving capabilities of large language models (LLMs) by emulating the cognitive processes of the human brain. This innovative model incorporates a range of intelligent mechanisms, including attention, memory, learning, reasoning, action selection, and decision-making.

While existing LLMs have made significant strides in their performance by utilizing specific prompts to generate chains of thought (CoT), OlaGPT takes a step further to simulate the intricate workings of the human mind. By incorporating six cognitive modules, OlaGPT aims to imitate key aspects of human cognition, such as attention extraction, memory retention, learning from feedback, logical reasoning, action selection, and decision-making.

Table of Contents

Enhancing Question-Answering through Simulated Human Cognition: The Comprehensive Flowchart of OlaGPT

The OlaGPT model follows a comprehensive flowchart to answer questions by simulating human cognition. It begins by analyzing the user’s question intention and proceeds to engage various cognitive modules, such as thinking templates, relevant resources, tools, facts, and error notes. Multiple agents are then created and executed to explore potential answers, with the final decision being made through a voting mechanism.

Superior Performance and Quality Assessment

To evaluate OlaGPT’s performance, the model was trained based on existing LLMs, including GPT-3, rather than from scratch. Two datasets were used for evaluation: AQuA, which consists of algebraic word problems, and E-KAR, containing knowledge-intensive analogical reasoning questions in both Chinese and English. The model’s accuracy was compared against GPT-3.5-turbo, Auto-CoT, and SC, while human evaluation was conducted to assess the quality and readability of OlaGPT-generated chains of thought in comparison to the baselines. The results demonstrated OlaGPT’s superior performance over state-of-the-art benchmarks.

Example

Let’s consider an example to showcase OlaGPT’s reasoning capabilities:

Q: What is the next number in the sequence 2, 4, 8, 16, …?

To solve this problem, OlaGPT employs the following chain of thought:

Recognize that this is a sequence pattern problem.
Utilize observation to identify that each number in the sequence is double the previous number.
Employ generalization to infer that the pattern involves multiplying by 2 each time.
Apply the pattern to the last number in the sequence to determine the next number: 16 * 2 = 32.
Validate the solution by using the checking tool: 32 divided by 2 equals 16.
Therefore, the answer is 32.

Throughout this reasoning process, OlaGPT’s cognitive modules play essential roles:

Intention Enhance directs attention to relevant information in the question, focusing on the “sequence of numbers” and the word “next.”
Memory recalls relevant knowledge about sequences and patterns, retrieving information from different libraries, including facts, tools, notes, and thinking.
Active Learning records user or expert feedback to enhance future problem-solving abilities.
Reasoning analyzes the sequence, employing observation, generalization, application, and checking tools to find the pattern.
Controller selects appropriate actions for each step of the reasoning process, such as observation for step 2 and multiplication for step 4.
Voting determines the best answer based on the confidence and accuracy of the reasoning module, providing the final response: “the answer is 32.”

In conclusion, OlaGPT represents a groundbreaking framework that endows LLMs with human-like problem-solving capabilities, effectively simulating various aspects of human cognition, such as attention, memory, reasoning, learning, and decision-making.

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