ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models

Concetti Chiave

LLMs provide effective common-sense knowledge infusion in Neuro-Symbolic HAR models, outperforming ontologies in data scarcity scenarios.

Sintesi

Context-aware Human Activity Recognition (HAR) is crucial for mobile computing applications. Existing solutions rely on supervised deep learning models but face limitations due to the scarcity of labeled data. Neuro-Symbolic AI (NeSy) offers a promising approach by infusing common-sense knowledge into HAR models. ContextGPT, a novel prompt engineering approach, leverages Large Language Models (LLMs) to retrieve common-sense knowledge about human activities and contexts effectively. The evaluation on public datasets shows that NeSy models infused with ContextGPT knowledge perform well in data scarcity scenarios, reducing human effort significantly.
Abstract:

Context-aware HAR is vital for mobile computing.
NeSy combines data-driven and knowledge-based approaches.
ContextGPT leverages LLMs for common-sense knowledge retrieval.
Evaluation on public datasets shows effectiveness in data scarcity scenarios.
Introduction:

Sensor data analysis for HAR extensively researched.
Context-aware approaches enhance recognition rates.
Deep learning classifiers require large labeled datasets.
Data Scarcity in HAR:

Supervised DL methods commonly used for sensor-based HAR.
Transfer learning, self-supervised learning proposed to mitigate labeled data scarcity.
Neuro-Symbolic HAR:

NeSy methods combine data-driven and knowledge-based approaches.
Logic-based models encode relationships between activities and contexts.
Using LLMs for HAR:

LLMs encode common-sense knowledge effectively.
ContextGPT retrieves context-consistent activities from LLMs.
Methodology:

NeSy framework processes sensor data and high-level context information.
ContextGPT reasons on contexts to derive consistent activities.
Results:

Knowledge infusion from ContextGPT outperforms purely data-driven baselines in data scarcity scenarios.
Competitive results achieved compared to ontology-based approaches with reduced human effort.

Statistiche

Neuro-Symbolic AI provides an interesting research direction to mitigate the issue of labeled data scarcity in Human Activity Recognition models.
Recent works show that pre-trained Large Language Models (LLMs) effectively encode common-sense knowledge about human activities.
In this work, we propose ContextGPT: a novel prompt engineering approach to retrieve from LLMs common-sense knowledge about the relationship between human activities and the context in which they are performed.
An extensive evaluation carried out on two public datasets shows how a NeSy model obtained by infusing common-sense knowledge from ContextGPT is effective in data scarcity scenarios, leading to similar (and sometimes better) recognition rates than logic-based approaches with a fraction of the effort.

Citazioni

"Neuro-Symbolic AI provides an interesting research direction to mitigate the issue of labeled data scarcity."
"ContextGPT: a novel prompt engineering approach to retrieve from LLMs common-sense knowledge about the relationship between human activities and the context."

Approfondimenti chiave tratti da

ContextGPT

by Luca Arrotta... alle arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06586.pdf

Domande più approfondite

How can the use of pre-trained Large Language Models impact the future development of Neuro-Symbolic AI

Pre-trained Large Language Models (LLMs) have the potential to significantly impact the future development of Neuro-Symbolic AI by providing a more efficient and scalable way to infuse common-sense knowledge into HAR models. LLMs, such as GPT-3, are trained on vast amounts of text data and can generate human-like text based on the input provided. By leveraging LLMs, researchers can extract common-sense knowledge about human activities and contexts without manually designing complex logic-based models or ontologies.
One key advantage is that LLMs can encode a wide range of domain-specific knowledge across different contexts and activities. This allows for more comprehensive coverage of possible scenarios without the need for extensive manual labor in creating detailed ontologies. Additionally, LLMs can adapt to new datasets and tasks with minimal effort compared to traditional ontology-based approaches.
Furthermore, advancements in LLM technology continue to improve their capabilities in understanding natural language and generating contextually relevant responses. As these models evolve, they will likely become even more adept at capturing nuanced relationships between activities and contexts, further enhancing their utility in Neuro-Symbolic AI systems for HAR.

What are the potential drawbacks of relying solely on LLMs for infusing common-sense knowledge into HAR models

While pre-trained Large Language Models (LLMs) offer significant advantages for infusing common-sense knowledge into Human Activity Recognition (HAR) models, there are also potential drawbacks associated with relying solely on LLMs:

Lack of semantic reasoning: LLMs operate based on statistical patterns learned from large text corpora rather than true semantic understanding. This may lead to inconsistencies or inaccuracies when inferring relationships between activities and contexts.

Model hallucinations: Due to the nature of data-driven text generation, LLMs may produce outputs that do not align with real-world constraints or logical reasoning. These "hallucinations" could introduce noise or incorrect information into the HAR model.

Limited interpretability: Understanding how an LLM arrives at its conclusions can be challenging due to their complex architecture and training process. This lack of transparency may hinder trust in the model's decisions within critical applications like HAR.

Data bias amplification: If pre-existing biases exist in the training data used for fine-tuning an LLM for HAR tasks, these biases could be amplified through automated decision-making processes based on biased textual patterns present in the model.

To mitigate these drawbacks, it is essential to combine the strengths of both symbolic reasoning methods like ontologies with data-driven approaches using LMMs effectively balancing interpretability with predictive power while ensuring robustness against biases.

How can advancements in natural language processing technologies influence the evolution of context-aware Human Activity Recognition systems

Advancements in natural language processing technologies have a profound influence on shaping the evolution of context-aware Human Activity Recognition (HAR) systems:
1. Enhanced contextual understanding: Improved NLP technologies enable HAR systems to better comprehend natural language descriptions related to user activities and environmental contexts leading towards more accurate activity recognition results.
2. Seamless integration with diverse datasets: Advanced NLP techniques facilitate seamless integration of diverse datasets containing textual descriptions alongside sensor data enabling richer contextual analysis which enhances overall system performance.
3. Real-time adaptation capabilities: With advancements such as transformer architectures allowing faster inference times coupled with continuous learning mechanisms enabled by NLP frameworks; context-aware HAR systems can dynamically adapt behavior based on changing environments improving responsiveness & accuracy over time
4. Personalized user experiences: Leveraging sophisticated NLP algorithms enables personalized activity recommendations tailored according individual preferences & habits fostering improved user engagement & satisfaction levels within various application domains including healthcare wellness etc
By harnessing these technological advancements effectively within context-aware Human Activity Recognition systems we pave way towards smarter more adaptive solutions capable offering enhanced functionality usability across diverse use cases benefiting end-users stakeholders alike