# Context Tuning for Retrieval Augmented Generation

**Authors:** Raviteja Anantha, Tharun Bethi, Danil Vodianik, Srinivas Chappidi  
**arXiv:** [2312.05708](https://arxiv.org/abs/2312.05708)  
**Date:** 2023-12-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.05708v1/

## Key points

- Context Tuning for Retrieval Augmented Generation (RAG) enhances RAG-based planning with context-seeking capabilities
- Comparison of retrieval methods on lightweight models and Large Language Models (LLMs)
- Effectiveness of context tuning in improving contextual understanding
- CoT augmentation enhances context retrieval without fine-tuning, while fine-tuning eliminates the need for CoT augmentation
- Context augmentation at plan generation stage reduces hallucinations
- Lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART outperforms GPT-4-based system
- Limitations of the model: no utilization of conversation history, hindering handling of explicit multi-turn instructions; limited capability to process complex tasks requiring multi-hop context retrieval
- Performance of planner model constrained by length of context window; longer windows improve performance but increase size and complexity
- Incorporating context compression techniques could improve end-to-end performance without increasing model size significantly
- Synthetic user profiles and personas generated due to privacy constraints for real-world data simulation
- Emphasis on ethical considerations by exclusively using synthetically generated data instead of real user information
- Conclusion: Context Tuning for RAG improves tool retrieval and plan generation through enhanced contextual understanding; significant improvements in semantic search and LLM-based planner accuracy demonstrated.

## Summary

The paper introduces Context Tuning for Retrieval Augmented Generation (RAG), a novel component that enhances RAG-based planning by equipping it with essential context-seeking capabilities to address incomplete or under-specified queries. The authors compare various retrieval methods applied to both lightweight models and Large Language Models (LLMs) and demonstrate the effectiveness of context tuning in improving contextual understanding. The study shows that CoT augmentation enhances context retrieval when fine-tuning is not applied, while fine-tuning the retrieval model eliminates the need for CoT augmentation. Additionally, context augmentation at the plan generation stage reduces hallucinations. The proposed lightweight model using Reciprocal Rank Fusion (RRF) with LambdaMART outperforms the GPT-4-based system. Furthermore, the model does not utilize conversation history, which hinders its ability to handle explicit multi-turn instructions containing anaphora or ellipsis. It also limits its capability to effectively process and respond to complex tasks requiring multi-hop context retrieval. Furthermore, the performance of the planner model is constrained by the length of the context window. While longer context windows can enhance performance, they increase model size and computational complexity. Incorporating context compression techniques could potentially improve end-to-end performance without significantly increasing model size. To simulate real-world data, synthetic user profiles and personas were generated due to privacy constraints. The study emphasizes ethical considerations by exclusively utilizing synthetically generated data instead of real user information. In conclusion, Context Tuning for RAG improves tool retrieval and plan generation by enhancing contextual understanding through smart context retrieval. The empirical results demonstrate significant improvements in semantic search and LLM-based planner accuracy.