Summary of Should We Fine-tune or Rag? Evaluating Different Techniques to Adapt Llms For Dialogue, by Simone Alghisi et al.
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue
by Simone Alghisi, Massimo Rizzoli, Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates the limitations of Large Language Models (LLMs) for response generation in human-machine dialogue. The authors analyze different LLM adaptation techniques applied to various dialogue types, including Open-Domain, Knowledge-Grounded, Task-Oriented, and Question Answering. Two base LLMs, Llama-2 and Mistral, are evaluated using fine-tuning and in-context learning methods across datasets specific to each dialogue type. The impact of incorporating external knowledge is assessed through Retrieval-Augmented Generation (RAG) and gold knowledge scenarios. Consistent evaluation and explainability criteria are applied for automatic metrics and human evaluation protocols. The results show that there is no universal best technique for adapting LLMs, as the efficacy of each technique depends on both the base LLM and dialogue type. Human evaluation is crucial to avoid false expectations based solely on automatic metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well Large Language Models (LLMs) work for generating responses in conversations between humans and machines. They test different ways to make these models better for different types of conversations, like talking about general topics or answering specific questions. The researchers use two kinds of LLMs and four types of conversations, and they see which methods work best for each one. They also look at how using extra information can help the models generate better responses. Overall, the study shows that there’s no single way to make an LLM better – it depends on both the model itself and the type of conversation. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Question answering » Rag » Retrieval augmented generation
