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SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation

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arxiv 2406.19215 v1 pith:5EB2NKTF submitted 2024-06-27 cs.CL

Zijun Yao , Weijian Qi , Liangming Pan , Shulin Cao , Linmei Hu , Weichuan Liu , Lei Hou , Juanzi Li This is my paper · ORCID
classification cs.CL
keywords seakrself-awareuncertaintyretrievaladaptiveknowledgecomplexgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge snippets, SeaKR re-ranks them based on LLM's self-aware uncertainty to preserve the snippet that reduces their uncertainty to the utmost. To facilitate solving complex tasks that require multiple retrievals, SeaKR utilizes their self-aware uncertainty to choose among different reasoning strategies. Our experiments on both complex and simple Question Answering datasets show that SeaKR outperforms existing adaptive RAG methods. We release our code at https://github.com/THU-KEG/SeaKR.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A per-hop evidence coverage verifier can stop multi-hop retrieval agents early, cutting search loops by up to 45% while preserving standardized-extractor exact match.

  2. Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking

    cs.IR 2025-09 conditional novelty 6.0 of 10

    Shifts in an LLM's hidden-state confidence, before and after a retrieved context, are used as a preference signal to fine-tune a reranker and to trigger retrieval only when initial confidence is low.

  3. Maximally-Informative Retrieval for State Space Model Generation

    cs.CL 2025-06 conditional novelty 6.0 of 10

    RICO ranks documents by how much they reduce an SSM's question perplexity, using gradient-document inner products, and matches BM25 while often beating E5 on answer quality without finetuning.

  4. Dynamic and Parametric Retrieval-Augmented Generation

    cs.CL 2025-06 unverdicted novelty 2.0 of 10

    A tutorial outline that categorizes recent RAG work into Dynamic RAG and Parametric RAG, and explains why both are needed.

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