# CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction

arXiv: https://arxiv.org/abs/2409.13701 · HTML: https://mlsanity.com/paper/2409.13701/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Knowledge Diffusion for Distillation](https://mlsanity.com/paper/2305.15712/) (2023)
- [Credit Card Fraud Detection Using Advanced Transformer Model](https://mlsanity.com/paper/2406.03733/) (2024)
- [Adapting LLMs for Efficient Context Processing through Soft Prompt Compression](https://mlsanity.com/paper/2404.04997/) (2024)
- [Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees](https://mlsanity.com/paper/2405.13290/) (2024)
- [Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model](https://mlsanity.com/paper/2404.17123/) (2024)
- [Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints](https://mlsanity.com/paper/2406.00610/) (2024)
- [Rough Set improved Therapy-Based Metaverse Assisting System](https://mlsanity.com/paper/2406.04465/) (2024)
- [Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques](https://mlsanity.com/paper/2403.05801/) (2024)
- [Advanced Payment Security System:XGBoost, LightGBM and SMOTE Integrated](https://mlsanity.com/paper/2406.04658/) (2024)
- [Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction](https://mlsanity.com/paper/2309.06584/) (2023)

## Built on by (most cited first, 3 of 3)

- [Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises](https://mlsanity.com/paper/2511.04020/) (2025)
- [Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback](https://mlsanity.com/paper/2511.03995/) (2025)
- [Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding](https://mlsanity.com/paper/2503.20227/) (2025)

## Abstract (arXiv)

Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.