  Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general
problem-solving skills. This performance discrepancy can be attributed to three
key factors: (1) Pre-training data, (2) Backbone architecture, and (3)
Instruction dataset. In this technical report, our main focus is on
investigating the impact of the third factor by leveraging VICUNA, a large
language model based on LLAMA, which has undergone fine-tuning on ChatGPT
conversations. To achieve this objective, we fine-tuned VICUNA using a
customized instruction dataset collection called FLANMINI. This collection
includes a subset of the large-scale instruction dataset known as FLAN, as well
as various code-related datasets and conversational datasets derived from
ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand
problem-solving skills. Our experimental findings strongly indicate that the
enhanced problem-solving abilities of our model, FLACUNA, are obtained through
fine-tuning VICUNA on the FLAN dataset, leading to significant improvements
across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly
available at https://huggingface.co/declare-lab/flacuna-13b-v1.0.
