LLM Guide
  • An introduction to Large Language Models
  • Understanding Pre-trained Language Models
  • Solutions to LLM Challenges
    • Prompt Engineering
    • Neuro-Symbolic Methods
    • Retrieval-Augmented Generation (RAG)
    • Honorable Mentions
  • Fine-Tuning
  • Supervised Fine-Tuning Strategies
    • Full Parameter Fine-Tuning
    • Half Fine-Tuning (HFT)
    • Parameter-Efficient Fine-Tuning (PEFT)
      • LoRA (Low-Rank Adaptation)
      • QLoRA (Quantized LoRA)
      • DoRA (Decomposed Low-Rank Adaptation)
      • NEFTune (Noise-Enhanced Fine-Tuning)
  • Fine-tuning Best Practices
  • Fine-tuning Using Ubiai (No-Codesolution)
  • Evaluation of Fine-Tuned Models
    • Evaluation Techniques
    • Task specific Evaluation Metrics
    • Popular Benchmarks
    • Best Practices for Model Evaluation
  • Directory of Links By Section
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Solutions to LLM Challenges

PreviousUnderstanding Pre-trained Language ModelsNextPrompt Engineering

Last updated 2 months ago

Beyond Fine-Tuning

In the last section of this guide, we explored the various challenges faced by pretrained LLMs, ranging from hallucinations and sensitivity to long-term context limitations. While fine-tuning has been a reliable approach for enhancing model performance in specific tasks, there are other solutions worth mentioning. In this section, we’ll examine a few interesting techniques that go beyond traditional fine-tuning, each bringing distinct advancements in accuracy, reasoning, and adaptability to tackle these persistent challenges.

Prompt Engineering
Neuro-Symbolic Methods
Retrieval-Augmented Generation (RAG)
Honorable Mentions