Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue
This work addresses the problem of selecting optimal adaptation techniques for LLMs in dialogue systems, providing insights for researchers and practitioners, though it is incremental as it builds on existing methods without introducing new paradigms.
The study evaluated fine-tuning and retrieval-augmented generation (RAG) techniques for adapting large language models (LLMs) like Llama-2 and Mistral across four dialogue types, finding no universal best method as efficacy depends on the base LLM and dialogue type, with human evaluation recommended to complement automatic metrics.
We study the limitations of Large Language Models (LLMs) for the task of response generation in human-machine dialogue. Several techniques have been proposed in the literature for different dialogue types (e.g., Open-Domain). However, the evaluations of these techniques have been limited in terms of base LLMs, dialogue types and evaluation metrics. In this work, we extensively analyze different LLM adaptation techniques when applied to different dialogue types. We have selected two base LLMs, Llama-2 and Mistral, and four dialogue types Open-Domain, Knowledge-Grounded, Task-Oriented, and Question Answering. We evaluate the performance of in-context learning and fine-tuning techniques across datasets selected for each dialogue type. We assess the impact of incorporating external knowledge to ground the generation in both scenarios of Retrieval-Augmented Generation (RAG) and gold knowledge. We adopt consistent evaluation and explainability criteria for automatic metrics and human evaluation protocols. Our analysis shows that there is no universal best-technique for adapting large language models as the efficacy of each technique depends on both the base LLM and the specific type of dialogue. Last but not least, the assessment of the best adaptation technique should include human evaluation to avoid false expectations and outcomes derived from automatic metrics.