Cover Image

TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Shaolei, Yu, Tian, Feng, Yang
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
by: Rahman, Subhey Sadi, et al.
Published: (2025)

Ranking Large Language Models without Ground Truth
by: Dhurandhar, Amit, et al.
Published: (2024)

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
by: Wei, Zhepei, et al.
Published: (2025)

Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
by: Liang, Kaiqu, et al.
Published: (2025)

Personas as a Way to Model Truthfulness in Language Models
by: Joshi, Nitish, et al.
Published: (2023)

Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts
by: Yu, Tian, et al.
Published: (2024)

TruthFlow: Truthful LLM Generation via Representation Flow Correction
by: Wang, Hanyu, et al.
Published: (2025)

On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models
by: Goel, Naman
Published: (2023)

When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
by: Wang, Shaowen, et al.
Published: (2025)

Overthinking the Truth: Understanding how Language Models Process False Demonstrations
by: Halawi, Danny, et al.
Published: (2023)

Soft Tokens, Hard Truths
by: Butt, Natasha, et al.
Published: (2025)

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
by: Li, Kenneth, et al.
Published: (2023)

The Geometries of Truth Are Orthogonal Across Tasks
by: Azizian, Waiss, et al.
Published: (2025)

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
by: Tan, Likun, et al.
Published: (2025)

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
by: Xiong, Guangzhi, et al.
Published: (2025)

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
by: Du, Hongzhe, et al.
Published: (2025)

Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
by: Tang, Zilu, et al.
Published: (2025)

Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)

Large Language Models are Skeptics: False Negative Problem of Input-conflicting Hallucination
by: Song, Jongyoon, et al.
Published: (2024)

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
by: Liu, Zihang, et al.
Published: (2025)

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
by: Wang, Shengyuan, et al.
Published: (2025)

Hierarchical Orthogonal Residual Spread for Precise Massive Editing in Large Language Models
by: Gu, Xiaojie, et al.
Published: (2026)

Hallucination is Inevitable: An Innate Limitation of Large Language Models
by: Xu, Ziwei, et al.
Published: (2024)

Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
by: Zhang, Hongxiang, et al.
Published: (2025)

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations
by: Luo, Wen, et al.
Published: (2026)

From Ground Truth to Measurement: A Statistical Framework for Human Labeling
by: Chew, Robert, et al.
Published: (2026)

Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
by: Zhang, Rongzhi, et al.
Published: (2025)

TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Intervention
by: Duan, Jinhao, et al.
Published: (2025)

(Im)possibility of Automated Hallucination Detection in Large Language Models
by: Karbasi, Amin, et al.
Published: (2025)

Scalable Token-Level Hallucination Detection in Large Language Models
by: Min, Rui, et al.
Published: (2026)

Knowledge Editing on Black-box Large Language Models
by: Song, Xiaoshuai, et al.
Published: (2024)

Spectral Editing of Activations for Large Language Model Alignment
by: Qiu, Yifu, et al.
Published: (2024)

Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
by: Zhang, Hao, et al.
Published: (2025)

LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
by: Veldanda, Akshaj Kumar, et al.
Published: (2024)

Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
by: Chen, Zhongzhi, et al.
Published: (2023)

CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
by: Shim, Jung-Woo, et al.
Published: (2025)

Principled Detection of Hallucinations in Large Language Models via Multiple Testing
by: Li, Jiawei, et al.
Published: (2025)

Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
by: Mündler, Niels, et al.
Published: (2023)

Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models
by: Shim, Jung-Woo, et al.
Published: (2025)