Cover Image

Do Retrieval Augmented Language Models Know When They Don't Know?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Youchao, Huang, Heyan, Liu, Yicheng, Dai, Rui, Wang, Xinglin, Zhang, Xingchen, Shi, Shumin, Deng, Yang
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
by: Mei, Zhiting, et al.
Published: (2025)

Can AI Assistants Know What They Don't Know?
by: Cheng, Qinyuan, et al.
Published: (2024)

Large Language Models Must Be Taught to Know What They Don't Know
by: Kapoor, Sanyam, et al.
Published: (2024)

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
by: Lee, Joosung, et al.
Published: (2026)

Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing
by: Yang, Diji, et al.
Published: (2025)

"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation
by: Thakur, Nandan, et al.
Published: (2023)

Do Large Language Models Know How Much They Know?
by: Prato, Gabriele, et al.
Published: (2025)

Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
by: Chen, Xinxi, et al.
Published: (2024)

Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness
by: Cheang, Chi Seng, et al.
Published: (2025)

Do Language Models Know When They're Hallucinating References?
by: Agrawal, Ayush, et al.
Published: (2023)

Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probing
by: Zhao, Raoyuan, et al.
Published: (2025)

World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertainty
by: Mei, Zhiting, et al.
Published: (2025)

KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
by: Li, Rui, et al.
Published: (2025)

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
by: Kumar, Sayantan, et al.
Published: (2026)

Fine-Tuned LLMs Know They Don't Know: A Parameter-Efficient Approach to Recovering Honesty
by: Shi, Zeyu, et al.
Published: (2025)

Subtopic-aware View Sampling and Temporal Aggregation for Long-form Document Matching
by: Zhou, Youchao, et al.
Published: (2024)

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
by: Mayne, Harry, et al.
Published: (2025)

KnowRL: Teaching Language Models to Know What They Know
by: Kale, Sahil, et al.
Published: (2025)

Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning
by: Stoisser, Josefa Lia, et al.
Published: (2025)

Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
by: Lachenmaier, Clara, et al.
Published: (2025)

Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell
by: Lu, Taiming, et al.
Published: (2024)

Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
by: Chen, Hao, et al.
Published: (2026)

"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
by: Di Scala, Daan, et al.
Published: (2026)

FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference
by: Liu, Runheng, et al.
Published: (2024)

Do Large Language Models Know What They Are Capable Of?
by: Barkan, Casey O., et al.
Published: (2025)

Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
by: Zhang, Yue, et al.
Published: (2026)

R-Tuning: Instructing Large Language Models to Say `I Don't Know'
by: Zhang, Hanning, et al.
Published: (2023)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)

Large Language Models Often Know When They Are Being Evaluated
by: Needham, Joe, et al.
Published: (2025)

CaRT: Teaching LLM Agents to Know When They Know Enough
by: Liu, Grace, et al.
Published: (2025)

I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token
by: Cohen, Roi, et al.
Published: (2024)

MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
by: Xiao, Xingchen, et al.
Published: (2026)

EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
by: Yeom, Jewon, et al.
Published: (2026)

Do Large Language Models Know What They Don't Know? Kalshibench: A New Benchmark for Evaluating Epistemic Calibration via Prediction Markets
by: Nel, Lukas
Published: (2025)

KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models
by: Zhang, Ruizhe, et al.
Published: (2024)

NanoKnow: How to Know What Your Language Model Knows
by: Gu, Lingwei, et al.
Published: (2026)

Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
by: Liu, Runheng, et al.
Published: (2026)

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
by: Zhu, Yuqi, et al.
Published: (2024)

Know What You Don't Know: Selective Prediction for Early Exit DNNs
by: Bajpai, Divya Jyoti, et al.
Published: (2025)

What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
by: Rezaeimanesh, Sara, et al.
Published: (2026)