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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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ChatABL: Abductive Learning via Natural Language Interaction With ChatGPT.

Tianyang Zhong, Yi Pan, Yutong Zhang

IEEE Transactions on Neural Networks and Learning Systems

|June 17, 2025

Summary

This study introduces ChatABL, a new method integrating large language models (LLMs) with abductive learning to enhance AI reasoning. ChatABL effectively bridges perception, language, and reasoning for complex tasks.

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Area of Science:

Artificial Intelligence
Cognitive Science

Background:

Large language models (LLMs) show promise in mathematical reasoning but struggle to integrate perception, language, and reasoning (PLR).
This limitation hinders their ability to perform complex reasoning tasks autonomously.

Purpose of the Study:

To propose ChatABL, a novel framework unifying LLMs and abductive learning (ABL) to overcome PLR integration challenges.
To enhance AI's ability to perform complex reasoning through natural language interaction.

Main Methods:

Integrating LLMs into an abductive learning (ABL) framework to create ChatABL.
Using LLMs to refine logical facts for the perception module by processing natural language domain knowledge.
Establishing a dynamic closed-loop system with feedback and automatic learning strategies for mutual performance enhancement.

Main Results:

ChatABL demonstrated superior reasoning capabilities in variable-length handwritten equation decipherment (HED) compared to state-of-the-art methods.
The system effectively unified perception, language understanding, and reasoning abilities.

Conclusions:

ChatABL represents a novel approach to achieving human-level cognitive abilities through natural language interaction with LLMs.
This framework offers a user-friendly and understandable method for complex AI reasoning.