  Advances towards more faithful and traceable answers of Large Language Models
(LLMs) are crucial for various research and practical endeavors. One avenue in
reaching this goal is basing the answers on reliable sources. However, this
Evidence-Based QA has proven to work insufficiently with LLMs in terms of
citing the correct sources (source quality) and truthfully representing the
information within sources (answer attributability). In this work, we
systematically investigate how to robustly fine-tune LLMs for better source
quality and answer attributability. Specifically, we introduce a data
generation pipeline with automated data quality filters, which can synthesize
diversified high-quality training and testing data at scale. We further
introduce four test sets to benchmark the robustness of fine-tuned specialist
models. Extensive evaluation shows that fine-tuning on synthetic data improves
performance on both in- and out-of-distribution. Furthermore, we show that data
quality, which can be drastically improved by proposed quality filters, matters
more than quantity in improving Evidence-Based QA.
