  We view the landscape of large language models (LLMs) through the lens of the
recently released BLOOM model to understand the performance of BLOOM and other
decoder-only LLMs compared to BERT-style encoder-only models. We achieve this
by evaluating the smaller BLOOM model variants (\textit{350m/560m} and
\textit{1b3/1b7}) on several NLP benchmark datasets and popular leaderboards.
We make the following observations: (1) BLOOM performance does not scale with
parameter size, unlike other LLMs like GPT and BERT. Experiments fine-tuning
BLOOM models show that the 560m variant performs similarly to or better than
the 1b7 variant, (2) Zero-shot cross-lingual and multi-lingual fine-tuning
experiments show that BLOOM is at par or worse than monolingual GPT-2 models,
and (3) Toxicity analysis of prompt-based text generation using the
RealToxicityPrompts dataset shows that the text generated by BLOOM is at least
17\% less toxic than GPT-2 and GPT-3 models.
