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.
翻译:我们从最近公布的BLOOM模型的透镜中查看了大型语言模型(LLMs)的景观,以了解BLOOM和其他只有解码器的LLMs与BERT式编码器的模型相比的性能。我们通过对若干NLP基准数据集和流行版头板上较小的BLOOM模型变体(\ textit{350m/560m}和\ textit{1b3/1b7})进行评估来实现这一目标。我们提出以下意见:(1) BLOOM的性能与GPT和BERT等其他LMs不同,与参数大小不同。实验微调BLOOM模型显示,560米变体的性能与1b7变体类似或更好,(2) Zero-shot跨语言和多语言微调实验显示,BLOOM比单语GPT-2模型低或差,(3) 使用RetoxityPrompts数据集对快速生成文本的毒性分析表明,BLOOM产生的文本至少比GPT-2和GPT-3模型毒性低17<unk> 。</s>