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TruthfulQA

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Showing 1–25 of 80 papers

TitleStatusHype
RLHF Workflow: From Reward Modeling to Online RLHFCode5
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination MitigationCode2
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful SpaceCode2
Tuning Language Models by ProxyCode2
Inference-Time Intervention: Eliciting Truthful Answers from a Language ModelCode2
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language ModelsCode2
Machine Unlearning in Large Language ModelsCode1
Tool-Augmented Reward ModelingCode1
Integrative Decoding: Improve Factuality via Implicit Self-consistencyCode1
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without TuningCode1
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and EthicsCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
Non-Linear Inference Time Intervention: Improving LLM TruthfulnessCode1
Instruction Tuning With Loss Over InstructionsCode1
Alleviating Hallucinations of Large Language Models through Induced HallucinationsCode1
RAIN: Your Language Models Can Align Themselves without FinetuningCode1
Red-Teaming Large Language Models using Chain of Utterances for Safety-AlignmentCode1
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
A Debate-Driven Experiment on LLM Hallucinations and Accuracy—0
Elastic Weight Consolidation for Full-Parameter Continual Pre-Training of Gemma2—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
Cost-Saving LLM Cascades with Early Abstention—0
Lower Layer Matters: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains—0
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