Large language models (LLMs), such as GPT-4, PaLM, and LLaMa, have been shown to achieve remarkable performance across a variety of natural language tasks. Recent advancements in instruction tuning bring LLMs with ability in following user's instructions and producing human-like responses. However, the high costs associated with training and implementing LLMs pose challenges to academic research. Furthermore, the availability of pretrained LLMs and instruction-tune datasets for Vietnamese language is limited. To tackle these concerns, we leverage large-scale instruction-following datasets from open-source projects, namely Alpaca, GPT4All, and
Doan, Vu-Thuan, Truong, Quoc-Truong, Nguyen, Duc-Vu, Nguyen, Vinh-Tiep, Luu, Thuy-Ngan Nguyen
Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions. To address these challenges, a fine-tuning process is employed, involving feedback and examples to refine models. The objective is to enhance AI models through continuous feedback loops, utilizing metrics such as cosine similarity, LLM evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on financial datasets, including the FinanceBench and RAG
Zhang, Liang, Jijo, Katherine, Setty, Spurthi, Chung, Eden, Javid, Fatima, Vidra, Natan, Clifford, Tommy
Research paper by Yuvanesh Anand and collaborators. Published 2023-11-06. Topics: Large language models, LLMs, GPT4All, open source ecosystem, technical overview.
Yuvanesh Anand, Zach Nussbaum, Adam Treat, Aaron Miller, Richard Guo, Ben Schmidt, GPT4All Community, Brandon Duderstadt, Andriy Mulyar
