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In our study, we compared the performance of Retrieval Augmented Generation (RAG) and Long-Context (LC) Large Language Models (LLMs), specifically Gemini-1.5 and GPT-4, on various public datasets for real and query-based tasks in English. We excluded summarization tasks without queries from our comparison. The datasets included NarrativeQA, Qasper, MultiFieldQA, HotpotQA, 2WikiMultihopQA, MuSiQue, QMSum from LongBench, and En.QA and EN.MC from ∞Bench. For evaluation metrics, we used F1 scores for open-ended QA tasks, accuracy for multi-choice QA tasks, and ROUGE score for summarization tasks. Our evaluation included three latest LLMs: Gemini-1.5-Pro supporting up to 1 million tokens, GPT-4O supporting 128k tokens, and GPT-3.5-Turbo supporting 16k tokens. Our results showed that LC consistently outperformed RAG in terms of average performance when adequately resourced. However,<kg>RAG's lower cost remained a significant advantage.</kg> To leverage the strengths of both approaches while reducing computation costs,<kg>we proposed Self-Route - a method that routes queries to RAG or LC based on model self-reflection.</kg> This approach maintained comparable performance to LC while significantly reducing costs.
- Compared performance of Retrieval Augmented Generation (RAG) and Long-Context (LC) Large Language Models (LLMs)
- LC consistently outperformed RAG in terms of average performance when adequately resourced
- RAG's lower cost remained a significant advantage
- Proposed Self-Route method to route queries to RAG or LC based on model self-reflection, maintaining comparable performance to LC while significantly reducing costs
Summary1. Two types of smart computer programs were compared to see which one worked better.
2. One program called LC did better than the other program called RAG most of the time when given enough resources.
3. RAG was cheaper to use, which was an important benefit.
4. A new method called Self-Route was suggested to decide when to use RAG or LC based on how well they think they are doing, keeping performance similar to LC but saving money.
5. This new method helps choose the best program for each task while saving costs.
Definitions- Compared: To look at two things and see how they are different or similar.
- Performance: How well something works or does its job.
- Resourced: Having enough tools or materials needed for a task.
- Advantage: Something good that gives you a benefit over others.
- Route: To direct or guide something in a certain direction.
Introduction
Language models have been making significant strides in natural language processing, with the latest advancements being large language models (LLMs) such as GPT-3 and Gemini-1.5. These LLMs have shown impressive performance on various tasks, including question answering and summarization. However, there is still room for improvement when it comes to handling long-context and query-based tasks.
In this research paper, we compare two approaches for handling long-context and query-based tasks: Retrieval Augmented Generation (RAG) and Long-Context (LC) Large Language Models. Specifically, we evaluate the performance of Gemini-1.5 and GPT-4 on various public datasets for real and query-based tasks in English.
Methodology
To conduct our evaluation, we used a range of public datasets that included NarrativeQA, Qasper, MultiFieldQA, HotpotQA, 2WikiMultihopQA, MuSiQue, QMSum from LongBench, and En.QA/EN.MC from ∞Bench. We excluded summarization tasks without queries from our comparison.
For evaluation metrics,we used F1 scores for open-ended QA tasks,accuracy for multi-choice QA tasks,and ROUGE score for summarization tasks.We chose these metrics as they are commonly used in evaluating language model performance.
We evaluated three latest LLMs: Gemini-1.5-Pro supporting up to 1 million tokens,GPT-4O supporting 128k tokens,and GPT-3.5-Turbo supporting 16k tokens.
Results
Our results showed that LC consistently outperformed RAG in terms of average performance when adequately resourced.This was expected since LC has the advantage of being trained on longer contexts compared to RAG. However,RAG's lower cost remained a significant advantage.
To leverage the strengths of both approaches while reducing computation costs,we proposed Self-Route - a method that routes queries to RAG or LC based on model self-reflection.This approach maintained comparable performance to LC while significantly reducing costs. This is achieved by having the model evaluate its own capabilities and determine whether it can handle the query efficiently or if it needs to be routed to another model.
Conclusion
In conclusion, our study showed that LC outperforms RAG in terms of average performance when adequately resourced.However, for those looking for a more cost-effective option, RAG remains a viable choice.To further improve performance while reducing costs,our proposed Self-Route method offers an effective solution by leveraging the strengths of both approaches.
This research provides valuable insights into the capabilities and limitations of LLMs when handling long-context and query-based tasks. It also highlights potential areas for improvement in future language models. We hope this study will serve as a useful reference for researchers and practitioners working in natural language processing.
