  This paper contains what the Georgetown InfoSense group has done in regard to
solving the challenges presented by TREC iKAT 2023. Our submitted runs
outperform the median runs by a significant margin, exhibiting superior
performance in nDCG across various cut numbers and in overall success rate. Our
approach uses a Generate-Retrieve-Generate method, which we've found to greatly
outpace Retrieve-Then-Generate approaches for the purposes of iKAT. Our
solution involves the use of Large Language Models (LLMs) for initial answers,
answer grounding by BM25, passage quality filtering by logistic regression, and
answer generation by LLMs again. We leverage several purpose-built Language
Models, including BERT, Chat-based, and text-to-transfer-based models, for text
understanding, classification, generation, and summarization. The official
results of the TREC evaluation contradict our initial self-evaluation, which
may suggest that a decrease in the reliance on our retrieval and classification
methods is better. Nonetheless, our findings suggest that the sequence of
involving these different components matters, where we see an essentiality of
using LLMs before using search engines.
