Summary of Investigating the Performance Of Retrieval-augmented Generation and Fine-tuning For the Development Of Ai-driven Knowledge-based Systems, by Robert Lakatos et al.
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems
by Robert Lakatos, Peter Pollner, Andras Hajdu, Tamas Joo
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 application of Retrieval-Augmented Generation (RAG) and Fine-tuning (FN) techniques for developing knowledge-based systems, such as ChatGPT or Bing, using generative large language models (G-LLM). The authors compare the performance of RAG and FN on GPT-J-6B, OPT-6.7B, LlaMA, and LlaMA-2 language models using ROUGE, BLEU, METEOR scores, and cosine similarity. The results demonstrate that RAG-based constructions outperform FN models, with a significant advantage in terms of hallucination. Additionally, the study highlights the importance of connecting RAG and FN models carefully, as connecting FN models with RAG can lead to a decrease in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make language models like ChatGPT or Bing better by using two techniques: Retrieval-Augmented Generation (RAG) and Fine-tuning (FN). The researchers compared these techniques on different language models, like GPT-J-6B or LlaMA. They used special scores to measure the performance of each technique, such as ROUGE, BLEU, and METEOR. The results show that RAG is better than FN for building knowledge-based systems. |
Keywords
* Artificial intelligence * Bleu * Cosine similarity * Fine tuning * Gpt * Hallucination * Llama * Rag * Retrieval augmented generation * Rouge
