This article summarizes various techniques and goals of language model finetuning, including knowledge injection and alignment, and discusses the effectiveness of different approaches such as instruction tuning and supervised fine-tuning.
A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.
NVIDIA and Georgia Tech researchers introduce RankRAG, a novel framework instruction-tuning a single LLM for top-k context ranking and answer generation. Aiming to improve RAG systems, it enhances context relevance assessment and answer generation.
This paper proposes a new method called MoRA for parameter-efficient fine-tuning of large language models (LLMs). The proposed method, MoRA, employs a square matrix to achieve high-rank updating, maintaining the same number of trainable parameters. The paper suggests that low-rank updating, as implemented in LoRA, may limit the ability of LLMs to effectively learn and memorize new knowledge. MoRA outperforms LoRA on memory-intensive tasks and achieves comparable performance on other tasks.
ChatQA, a new family of conversational question-answering (QA) models developed by NVIDIA AI. These models employ a unique two-stage instruction tuning method that significantly improves zero-shot conversational QA results from large language models (LLMs). The ChatQA-70B variant has demonstrated superior performance compared to GPT-4 across multiple conversational QA datasets.
Comprehensive guide to ChatGPT API for newbies
