  This study delves into the capabilities and limitations of Large Language
Models (LLMs) in the challenging domain of conditional question-answering.
Utilizing the Conditional Question Answering (CQA) dataset and focusing on
generative models like T5 and UL2, we assess the performance of LLMs across
diverse question types. Our findings reveal that fine-tuned LLMs can surpass
the state-of-the-art (SOTA) performance in some cases, even without fully
encoding all input context, with an increase of 7-8 points in Exact Match (EM)
and F1 scores for Yes/No questions. However, these models encounter challenges
in extractive question answering, where they lag behind the SOTA by over 10
points, and in mitigating the risk of injecting false information. A study with
oracle-retrievers emphasizes the critical role of effective evidence retrieval,
underscoring the necessity for advanced solutions in this area. Furthermore, we
highlight the significant influence of evaluation metrics on performance
assessments and advocate for a more comprehensive evaluation framework. The
complexity of the task, the observed performance discrepancies, and the need
for effective evidence retrieval underline the ongoing challenges in this field
and underscore the need for future work focusing on refining training tasks and
exploring prompt-based techniques to enhance LLM performance in conditional
question-answering tasks.
