A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case
This work provides practical guidance for developers building domain-specific chatbots by comparing fine-tuning approaches and evaluation methods, though it is incremental in nature.
This research compared LLM fine-tuning methods (QLoRA, RAFT, RLHF) and evaluation metrics using a travel chatbot dataset, finding that Mistral with RAFT and RLHF performed best according to human evaluation and GPT-4 metrics, while traditional NLP metrics did not align with human judgments.
This research compares large language model (LLM) fine-tuning methods, including Quantized Low Rank Adapter (QLoRA), Retrieval Augmented fine-tuning (RAFT), and Reinforcement Learning from Human Feedback (RLHF), and additionally compared LLM evaluation methods including End to End (E2E) benchmark method of "Golden Answers", traditional natural language processing (NLP) metrics, RAG Assessment (Ragas), OpenAI GPT-4 evaluation metrics, and human evaluation, using the travel chatbot use case. The travel dataset was sourced from the the Reddit API by requesting posts from travel-related subreddits to get travel-related conversation prompts and personalized travel experiences, and augmented for each fine-tuning method. We used two pretrained LLMs utilized for fine-tuning research: LLaMa 2 7B, and Mistral 7B. QLoRA and RAFT are applied to the two pretrained models. The inferences from these models are extensively evaluated against the aforementioned metrics. The best model according to human evaluation and some GPT-4 metrics was Mistral RAFT, so this underwent a Reinforcement Learning from Human Feedback (RLHF) training pipeline, and ultimately was evaluated as the best model. Our main findings are that: 1) quantitative and Ragas metrics do not align with human evaluation, 2) Open AI GPT-4 evaluation most aligns with human evaluation, 3) it is essential to keep humans in the loop for evaluation because, 4) traditional NLP metrics insufficient, 5) Mistral generally outperformed LLaMa, 6) RAFT outperforms QLoRA, but still needs postprocessing, 7) RLHF improves model performance significantly. Next steps include improving data quality, increasing data quantity, exploring RAG methods, and focusing data collection on a specific city, which would improve data quality by narrowing the focus, while creating a useful product.