  The Retrieval-Augmented Language Model (RALM) has shown remarkable
performance on knowledge-intensive tasks by incorporating external knowledge
during inference, which mitigates the factual hallucinations inherited in large
language models (LLMs). Despite these advancements, challenges persist in the
implementation of RALMs, particularly concerning their reliability and
traceability. To be specific, the irrelevant document retrieval may result in
unhelpful response generation or even deteriorate the performance of LLMs,
while the lack of proper citations in generated outputs complicates efforts to
verify the trustworthiness of the models. To this end, we propose a novel
self-reasoning framework aimed at improving the reliability and traceability of
RALMs, whose core idea is to leverage reasoning trajectories generated by the
LLM itself. The framework involves constructing self-reason trajectories with
three processes: a relevance-aware process, an evidence-aware selective
process, and a trajectory analysis process. We have evaluated our framework
across four public datasets (two short-form QA datasets, one long-form QA
dataset, and one fact verification dataset) to demonstrate the superiority of
our method, which can outperform existing state-of-art models and can achieve
comparable performance with GPT-4, while only using 2,000 training samples.
