CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
Huanshuo Liu, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai, Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu
TL;DR
CtrlA introduces an innovative representation-based framework for adaptive retrieval-augmented generation, enhancing LLM honesty and confidence monitoring to improve factual accuracy and retrieval timing.
Contribution
This work pioneers an inherent control-based approach for adaptive RAG, focusing on representation features to guide retrieval and behavior of LLMs.
Findings
Outperforms existing adaptive RAG methods across various tasks.
Honesty steering improves LLM honesty and reliability.
Confidence monitoring effectively indicates when to trigger retrieval.
Abstract
Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) with retrieved external knowledge. Adaptive RAG enhances this approach by enabling dynamic retrieval during generation, activating retrieval only when the query exceeds LLM's internal knowledge. Existing methods primarily focus on detecting LLM's confidence via statistical uncertainty. Instead, we present the first attempts to solve adaptive RAG from a representation perspective and develop an inherent control-based framework, termed \name. Specifically, we extract the features that represent the honesty and confidence directions of LLM and adopt them to control LLM behavior and guide retrieval timing decisions. We also design a simple yet effective query formulation strategy to support adaptive retrieval. Experiments show that \name is superior to…
Peer Reviews
Decision·Submitted to ICLR 2025
1. CTRLA introduces a method for adaptive retrieval, focusing on honesty and confidence features within the LLM's representation space, a departure from conventional uncertainty-based approaches. 2. The experiments reveal CTRLA’s effectiveness in generating accurate and relevant responses, with enhanced performance in both short-form and long-form QA tasks. 3. The approach optimizes retrieval timing, resulting in fewer unnecessary retrievals. 4. The CTRLA framework is lightweight and does not he
1. The experiments cover several QA tasks, and additional datasets that may not be memorized by the LLM could have further validated the framework's robustness, especially across rapidly changing topic datasets. 2. There are many similar works described as "knowledge conflicts", but the author seems to miss them both for introducing and as baselines, such as 2.1 Xie, Jian, et al. "Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts." arXiv
- The proposed method is interesting and effective. - The paper is well-written and easy to follow.
- The method requires accessing the internal structure of LLMs, which may not be available for some competitive commercial LLMs such as o1 and limits its applicability. - The approach relies heavily on PCA and detailed layer-wise feature extraction, potentially leading to high computational costs and processing time, especially with larger models. The principle component selection process is also not discussed since the first principle component might not represent the required direction. - T
This paper proposes to address Adaptive Retrieval-Augmented Generation by focusing on retrieval timing to reduce hallucinations. It takes an interpretable approach by analyzing internal mechanisms like honesty and confidence, providing clearer insights into model behavior. Experimental results show meaningful improvements, suggesting that the proposed method contributes effectively to retrieval-augmented generation tasks.
1. The paper doesn’t explain why the methods are crucial for Adaptive Retrieval-Augmented Generation (ARAG). The section Confidence Monitoring as Retrieval Trigger seems directly relevant to ARAG, while the section Honesty Steering and Search Query Formulation seems to be unrelated to the main goal. The Honesty Steering part has already been introduced in similar work[1], which makes the technical contribution of the paper incremental. The methods aren’t presented in a clear, motivating way, mak
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Taxonomy
TopicsAlgorithms and Data Compression · Advanced Data Storage Technologies · Neural Networks and Applications
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Sparse Evolutionary Training · Focus · WordPiece · Linear Warmup With Linear Decay · Weight Decay · Attention Dropout · Linear Layer · Byte Pair Encoding