Categories: AI/ML Research

5 Problems Encountered Fine-Tuning LLMs with Solutions

Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction.

Custom Fine-Tuning for Domain-Specific LLMs

Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of…

May 15, 2025

In "AI/ML Research"

Understanding RAG Part IX: Fine-Tuning LLMs for RAG

Be sure to check out the previous articles in this series: •

April 11, 2025

In "AI/ML Research"

Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages

This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a…

August 2, 2024

In "FAANG"

AI Generated Robotic Content