5 MAIN RESULTS

In Table 4, we present the comparative prompting results of our models (AdaptLLM) against the
general language model (General LLM) and the models that have gone vanilla domain-adaptive pre-
training on raw corpora (DAPT). On various tasks in the three different domains, the use of raw
texts in DAPT adversely affects the performance. However, the reformatting of raw texts and the
inclusion of general instructions in AdaptLLM manage to counteract this effect, resulting in better
results than the general language model.

Table 4: Domain-specific task performance of general large language model (General LLM),
vanilla domain-adaptive pretraining (DAPT), and ours (AdaptLLM) in prompting evaluation.
We also display prompting results of other models including MedAlpaca (Han et al., 2023) in
biomedicine, BloombergGPT (Wu et al., 2023b) in finance, and LexGPT (Lee, 2023) in law.

PubMedQA ChemProt MQP RCT UMSLE AVERAGE
ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION