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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Related Experiment Video

Updated: Sep 23, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

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RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC

Hamid Gharagozlou1, Javad Mohammadzadeh1, Azam Bastanfard1

  • 1Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.

Computational Intelligence and Neuroscience
|May 16, 2022
PubMed
Summary

This study introduces RLAS-BIABC, a novel method for answer selection in question answering systems. It effectively handles imbalanced data using reinforcement learning and an improved artificial bee colony algorithm, achieving state-of-the-art results.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Answer selection is crucial for open-domain question answering.
  • Existing methods struggle with imbalanced datasets (more negative than positive answer pairs).
  • This imbalance significantly degrades classifier performance.

Purpose of the Study:

  • To propose RLAS-BIABC, a new method for answer selection.
  • To address data imbalance issues in answer selection tasks.
  • To improve the overall performance of question answering systems.

Main Methods:

  • Utilizes attention mechanism-based Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) word embeddings.
  • Employs an improved Artificial Bee Colony (ABC) algorithm for pretraining and a reinforcement learning (RL) algorithm for training.
  • Introduces a mutual learning technique to enhance the ABC algorithm by modifying candidate solutions based on fitness.

Main Results:

  • The proposed RLAS-BIABC method effectively handles imbalanced classification by framing it as a sequential decision-making process.
  • The RL agent is trained with rewards favoring minority classes, optimizing policy weights initialized by the improved ABC algorithm.
  • Evaluated on LegalQA, TrecQA, and WikiQA datasets, RLAS-BIABC demonstrates state-of-the-art performance.

Conclusions:

  • RLAS-BIABC offers a robust solution for answer selection, particularly in scenarios with imbalanced data.
  • The combination of BERT, LSTM, RL, and an enhanced ABC algorithm leads to superior performance.
  • The method represents a significant advancement in open-domain question answering technology.