  Recent work has aimed to improve LLM generations by filtering out
hallucinations, thereby improving the precision of the information in
responses. Correctness of a long-form response, however, also depends on the
recall of multiple pieces of information relevant to the question. In this
paper, we introduce Atomic Self-Consistency (ASC), a technique for improving
the recall of relevant information in an LLM response. ASC follows recent work,
Universal Self-Consistency (USC) in using multiple stochastic samples from an
LLM to improve the long-form response. Unlike USC which only focuses on
selecting the best single generation, ASC picks authentic subparts from the
samples and merges them into a superior composite answer. Through extensive
experiments and ablations, we show that merging relevant subparts of multiple
samples performs significantly better than picking a single sample. ASC
demonstrates significant gains over USC on multiple factoids and open-ended QA
datasets - ASQA, QAMPARI, QUEST, ELI5 with ChatGPT and Llama2. Our analysis
also reveals untapped potential for enhancing long-form generations using
approach of merging multiple samples.
