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Showing 51–80 of 80 papers

TitleStatusHype
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
Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation—0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections—0
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs—0
More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment—0
Multi-Reference Preference Optimization for Large Language Models—0
A Debate-Driven Experiment on LLM Hallucinations and Accuracy—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
Shadows in the Attention: Contextual Perturbation and Representation Drift in the Dynamics of Hallucination in LLMs—0
SkillAggregation: Reference-free LLM-Dependent Aggregation—0
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency—0
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