# Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device

**Authors:** Juntae Lee, Jihwan Bang, Seunghan Yang, Kyuhong Shim, Simyung Chang  
**arXiv:** [2502.15134](https://arxiv.org/abs/2502.15134)  
**Date:** 2025-02-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.15134v1/

## Key points

- Precision is paramount in retrieval-augmented generation (RAG) with large language models (LLMs), especially in specialized domains.
- Domain-specific RAG has emerged as a valuable tool to cater to the need for precision, allowing LLMs to be finely tuned for specific target domains early on.
- Domain-specific RAG is particularly beneficial in resource-constrained environments like edge devices where reliable performance from small-scale LLMs is crucial for tasks such as personalization.
- Traditional domain-specific RAG often relies on complex reasoning techniques like chain-of-thought (CoT), which can be computationally expensive and challenging for small-scale LLMs.
- A new approach called Chain of Rank (CoR) simplifies the reasoning process by focusing on ranking the reliability of input external documents rather than intricate reasoning steps.
- CoR reduces computational complexity while maintaining high accuracy, leading to state-of-the-art results in benchmarks and efficacy analysis.
- CoR enhances large language models for domain-specific RAG in edge devices by streamlining the reasoning process and prioritizing document reliability over complex reasoning steps, showing significant promise for practical applications.

## Summary

In the realm of retrieval-augmented generation (RAG) with large language models (LLMs), precision is paramount, especially in specialized domains. To cater to this need for precision, domain-specific RAG has emerged as a valuable tool. This allows LLMs to be finely tuned for specific target domains early on and is particularly beneficial in resource-constrained environments such as edge devices. In these environments, reliable performance from small-scale LLMs is crucial for tasks like personalization. Traditional domain-specific RAG often relies on complex reasoning techniques like chain-of-thought (CoT). However, these can be computationally expensive and challenging for small-scale LLMs to grasp. In response to this challenge, a new approach called Chain of Rank (CoR) has been proposed. CoR simplifies the reasoning process by shifting the focus from intricate reasoning to a straightforward ranking of the reliability of input external documents. By doing so, CoR reduces computational complexity while maintaining high accuracy. The effectiveness of CoR has been demonstrated through achieving state-of-the-art results in benchmarks and thorough efficacy analysis. This innovative framework enhances large language models for domain-specific RAG in edge devices by streamlining the reasoning process and prioritizing document reliability over intricate reasoning steps. As we delve deeper into the methodology behind CoR and its implications for specialized domains and resource-constrained environments, it becomes evident that this approach holds significant promise for advancing retrieval-augmented generation with large language models in practical applications.