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Related Experiment Video

Updated: Mar 10, 2026

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

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

Published on: December 6, 2024

Comparing vector-based and Bayesian memory models using large-scale datasets: User-generated hashtag and tag

Clayton Stanley¹, Michael D Byrne¹

¹Department of Psychology, Rice University.

Psychological Methods

|December 6, 2016

Summary

This summary is machine-generated.

Past user behavior strongly predicts future tag choices on social media. Models integrating this behavior, like the random permutation model, significantly improve predictions of declarative memory retrieval.

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Area of Science:

Cognitive Science
Computational Linguistics
Human-Computer Interaction

Background:

Social media platforms generate vast amounts of user-created content, offering novel opportunities to study human declarative memory.
Declarative memory models can be applied to understand user behavior in tagging and hashtag selection on platforms like Twitter and Stack Overflow.

Purpose of the Study:

To evaluate the predictive accuracy of two cognitively plausible declarative memory models for user-selected tags.
To investigate the influence of past user behavior and context on tag prediction.
To explore potential modifications to memory model architectures for improved human data fit.

Main Methods:

Applied an ACT-R based Bayesian model and a random permutation vector-based model to millions of social media posts and tweets.
Framed hashtag and tag selection as declarative memory retrieval problems.
Incorporated past user tag behavior and context into the models, linking ACT-R's attentional weights to natural language processing methods.

Main Results:

Past user tag behavior is a significant predictor of future tag selection.
The random permutation model, enhanced with past behavior, achieved comparable performance to the Bayesian model without word order.
Word order was not a strong predictor; context compression in the random permutation model was key.

Conclusions:

User behavior is a critical component in declarative memory retrieval models for predicting tag use.
The random permutation model's effectiveness stems from efficient context compression, not word order representation.
Modifications to memory model architectures, incorporating user behavior and context, can enhance predictive accuracy across various domains.