Papers with three-stage architecture
monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering (2022.emnlp-main)
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| Challenge: | Existing approaches to the Conversational Question Answering task have used multi-task learning to solve the task. |
| Approach: | They propose to use multi-task learning to improve the ORConvQA task by sharing the reranker and reader’s learned structure in a generative model. |
| Outcome: | The proposed model outperforms baseline models on the OR-QuAC and OR-CoQA datasets and significantly outperformed existing strong baseline models. |
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion (2026.acl-long)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) suffer from a performance bottleneck . Existing approaches like Offsite-Tuning (OT) secure the LLMs IP . |
| Approach: | They propose a framework that replaces weak adapters with a unified, powerful Proxy Small Language Model (SLM) they propose 'resource-friendly' compression and 'robust optimization' to handle data heterogeneity. |
| Outcome: | Experiments show that FedProxy outperforms OT and centralized fine-tuning methods. |