Large language models (LLMs) are widely used in question-answering (QA) systems but often generate information with hallucinations. Retrieval-augmented generation (RAG) offers a potential remedy, yet the uneven retrieval quality and irrelevant contents may distract LLMs.In this work, we address these issues at the generation phase by treating RAG as a multi-document QA task.We propose a novel decoding strategy, Dynamic Contrastive Decoding, which dynamically amplifies knowledge from selected documents during the generation phase. involves constructing inputs batchwise, designing new selection criteria to identify documents worth amplifying, and applying contrastive decoding with a specialized weight calculation to adjust the final logits used for sampling answer tokens. Zero-shot experimental results on ALCE-ASQA, NQ, TQA and PopQA benchmarks show that our method outperforms other decoding strategies. Additionally, we conduct experiments to validate the effectiveness of our selection criteria, weight calculation, and general multi-document scenarios. Our method requires no training and can be integrated with other methods to improve the RAG performance. Our codes will be publicly available at https://github.com/JulieJin-km/Dynamic_Contrastive_Decoding.

Synthesizing QA pairs via question generator (QG) for data augmentation is widely used in Machine Reading Comprehension (MRC), especially in data-scarce scenarios like limited labeled data or domain adaptation. However, the quality of generated QA pairs varies, and it is necessary to select the ones with high quality from them. Existing approaches focus on downstream metrics to choose QA pairs, which lacks generalization across different metrics and datasets. In this paper, we propose a general selection method that employs a generative large pre-trained language model as a reward model in a Reinforcement Learning (RL) framework for the training of the selection agent. Our experiments on both generative and extractive datasets demonstrate that our selection method leads to better downstream performance. We also find that using the large language model (LLM) as a reward model is more beneficial than using it as a direct selector or QA model. Furthermore, we assess the selected QA pairs from multiple angles, not just downstream metrics, highlighting their superior quality compared to other methods. Our work has better flexibility across metrics, provides interpretability for the selected data, and expands the potential of leveraging generative large language models in the field of MRC and RL training. Our code is available at https://github.com/JulieJin-km/LLM_RL_Selection.