Evaluating Design Choices in Verifiable Generation with Open-source Models

Verifiable generation is introduced to improve the transparency and trustworthiness of outputs produced by large language models (LLMs).Recent studies observe that open-source models struggle to include accurate citations to supporting documents in their generation with in-context learning, in contrast to the strong performance demonstrated by proprietary models.Our work aims to reveal the critical design choices that can benefit open-source models, including generation pipelines, fine-tuning methods, and inference-time compute techniques.We consider three generation pipelines, producing the outputs directly or decomposing the generation into subtasks.These generation pipelines are fine-tuned using supervised fine-tuning and preference-based optimization including further fine-tuning with rejection sampling data and direct preference optimization (DPO).The construction of preference data with varying content and citation diversity is also investigated.Additionally, we examine the benefit of an additional reranking step.With four open-source models, our experiments show that directly generating the outputs achieves the best performance.Compared to other fine-tuning methods, DPO that computes training signals from contrastive pairs consistently yields better performance, and it reaches the peak performance when the contrastive pairs are constructed with sufficient content diversity.We also find that reranking can further boost the performance of verifiable generation systems, but the marginal improvement might not justify the additional cost.