LARA: Linguistic-Adaptive Retrieval-Augmented LLMs for Multi-Turn Intent Classification

Concepts de base

LARA enhances multi-turn intent classification with Linguistic-Adaptive Retrieval-Augmented LLMs.

Résumé

The paper introduces LARA, a framework designed to improve accuracy in multi-turn intent classification tasks across six languages. By combining a fine-tuned smaller model with a retrieval-augmented mechanism integrated within the architecture of LLMs, LARA dynamically utilizes past dialogues and relevant intents to enhance context understanding. The adaptive retrieval techniques also strengthen cross-lingual capabilities without extensive retraining. Comprehensive experiments show that LARA outperforms existing methods by 3.67% in average accuracy.

Introduction:

• Chatbots play a crucial role in e-commerce platforms for efficient customer service.
• Multi-turn conversations pose challenges due to contextual factors and evolving user intentions.

Problem Formulation:

• Single-turn and multi-turn intent classification differ in complexity and context dependency.

LARA Framework:

• Combines XLM-based model training with in-context learning for multi-turn dialogue classification.

Experiments:

• Datasets from eight markets used to evaluate performance metrics like accuracy.

Results and Discussions:

• Comparison of LARA with baselines shows improved performance across different prompts.

Conclusion:

• LARA offers an effective solution for multi-turn intent classification, enhancing accuracy and efficiency.

Stats

Comprehensive experiments demonstrate that LARA achieves state-of-the-art performance on multi-turn intent classification tasks, enhancing the average accuracy by 3.67% compared to existing methods.

Citations

Idées clés tirées de

LARA

by Liu Junhua,T... à arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16504.pdf

Questions plus approfondies

How can the linguistic-adaptive approach of LARA be applied to other NLP tasks

LARA's linguistic-adaptive approach can be applied to various NLP tasks by leveraging past dialogues and context understanding. For tasks like sentiment analysis, the model can adapt to different conversational tones and nuances in user expressions. In machine translation, LARA could utilize previous translations to enhance accuracy and fluency in multilingual conversations. Additionally, for text summarization, the framework could benefit from historical context to generate more coherent and informative summaries.

What potential limitations or biases could arise from relying heavily on past dialogues for context understanding

Relying heavily on past dialogues for context understanding may introduce limitations and biases in the system. One potential limitation is the risk of overfitting to specific patterns or intents present in the training data, leading to a lack of generalization when faced with new or unseen scenarios. Biases may arise if the historical dialogues contain skewed or incomplete information that influences decision-making processes within the model. Moreover, outdated or irrelevant information from past interactions could impact the accuracy of intent classification in dynamic conversational contexts.

How might advancements in large-scale language models impact the future development of frameworks like LARA

Advancements in large-scale language models are likely to have a significant impact on frameworks like LARA. Improved language models with enhanced capabilities for contextual understanding and multi-turn dialogue processing will enable more sophisticated applications of linguistic-adaptive approaches. These advancements may lead to better performance in intent classification tasks across multiple languages and domains by providing richer contextual embeddings and more accurate predictions based on comprehensive dialogue histories. Furthermore, as language models continue to evolve, they may offer enhanced support for zero-shot learning scenarios where minimal annotated data is available for training robust intent recognition systems like LARA.