fine-tuning results in Figure 3, continued training on the reading comprehension texts consistently
enhances model performance when fine-tuning on domain-specific tasks. The fine-tuning and do-
main knowledge probing improvements (detailed in Appendix A) provide empirical evidence that
the reading comprehension texts indeed imbue the general language model with domain knowledge.
Furthermore, it’s noteworthy that Read. Compre. outperforms Raw Text in all the adapted domains
in the fine-tuning results. This improvement can be attributed to the fact that the appended compre-
hension tasks naturally create a “multi-task instruction tuning” setting, which benefits single-task
fine-tuning, as discussed by Longpre et al. (2023a).
Prompting Ability. Our approach focuses on enhancing prompting ability through the comprehen-
sion tasks. To assess the effectiveness of each comprehension task type, we employ general LLM
ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION