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Showing 26–50 of 80 papers

TitleStatusHype
LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop—0
DYNAMAX: Dynamic computing for Transformers and Mamba based architectures—0
A Debate-Driven Experiment on LLM Hallucinations and Accuracy—0
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer—0
Elastic Weight Consolidation for Full-Parameter Continual Pre-Training of Gemma2—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
GRATH: Gradual Self-Truthifying for Large Language Models—0
Harmonic LLMs are Trustworthy—0
Investigating Data Contamination in Modern Benchmarks for Large Language Models—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
LokiLM: Technical Report—0
Lower Layer Matters: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused—0
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains—0
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models—0
PRobELM: Plausibility Ranking Evaluation for Language Models—0
Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs—0
Reducing LLM Hallucinations using Epistemic Neural Networks—0
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning—0
Sample, Don't Search: Rethinking Test-Time Alignment for Language Models—0
Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models—0
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models—0
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation—0
Self-Evaluation Improves Selective Generation in Large Language Models—0
Semantic Consistency for Assuring Reliability of Large Language Models—0
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