Investigating Answerability of LLMs for Long-Form Question Answering
Authors: name: Meghana Moorthy Bhat, name: Rui Meng, name: Ye Liu, name: Yingbo Zhou, name: Semih Yavuz

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)