As we embark on a new era of LLMs, it becomes increasingly crucial to
understand their capabilities, limitations, and differences. Toward making
further progress in this direction, we strive to build a deeper understanding
of the gaps between massive LLMs (e.g., ChatGPT) and smaller yet effective
open-source LLMs and their distilled counterparts. To this end, we specifically
focus on long-form question answering (LFQA) because it has several practical
and impactful applications (e.g., troubleshooting, customer service, etc.) yet
is still understudied and challenging for LLMs. We propose a
question-generation method from abstractive summaries and show that generating
follow-up questions from summaries of long documents can create a challenging
setting for LLMs to reason and infer from long contexts. Our experimental
results confirm that: (1) our proposed method of generating questions from
abstractive summaries pose a challenging setup for LLMs and shows performance
gaps between LLMs like ChatGPT and open-source LLMs (Alpaca, Llama) (2)
open-source LLMs exhibit decreased reliance on context for generated questions
from the original document, but their generation capabilities drop
significantly on generated questions from summaries -- especially for longer
contexts (>1024 tokens)

从长文摘要生成问题的方法对于 LLMs 的性能造成挑战，表明 LLMs（如 ChatGPT）与开源 LLMs（Alpaca、Llama）之间存在性能差距。