Papers with Retrieval-augmented generation framework
Unveiling the Magic: Investigating Attention Distillation in Retrieval-Augmented Generation (2024.naacl-short)
Copied to clipboard
| Challenge: | Retrieval-augmented generation framework addresses the limitations of large language models by enabling real-time knowledge updates for more accurate answers. |
| Approach: | They propose to use attention distillation to improve retrieval-augmented language models' learning performance by identifying key factors influencing their workflow and proposing indicators for optimizing models’ training methods and avoiding ineffective training. |
| Outcome: | The proposed framework improves the learning performance of large language models in the training phase but also reduces the impact of ineffective training. |
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts. |
| Approach: | They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
| Outcome: | The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions. |