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Showing 51–80 of 80 papers

TitleStatusHype
Efficiently Deploying LLMs with Controlled Risk—0
Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs—0
Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models—0
Lower Layer Matters: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused—0
LokiLM: Technical Report—0
metabench -- A Sparse Benchmark to Measure General Ability in Large Language ModelsCode0
VarBench: Robust Language Model Benchmarking Through Dynamic Variable PerturbationCode0
Steering Without Side Effects: Improving Post-Deployment Control of Language ModelsCode0
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided DecodingCode0
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language ModelsCode0
Multi-Reference Preference Optimization for Large Language Models—0
Harmonic LLMs are Trustworthy—0
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning—0
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language ModelsCode0
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition DynamicsCode0
PRobELM: Plausibility Ranking Evaluation for Language Models—0
SaGE: Evaluating Moral Consistency in Large Language ModelsCode0
Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation—0
LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop—0
GRATH: Gradual Self-Truthifying for Large Language Models—0
Reducing LLM Hallucinations using Epistemic Neural Networks—0
Self-Evaluation Improves Selective Generation in Large Language Models—0
Uncertainty-aware Language Modeling for Selective Question Answering—0
Investigating Data Contamination in Modern Benchmarks for Large Language Models—0
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models—0
Instruction Tuning with Human CurriculumCode0
Semantic Consistency for Assuring Reliability of Large Language Models—0
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human FeedbackCode0
Measuring Reliability of Large Language Models through Semantic ConsistencyCode0
Teaching language models to support answers with verified quotes—0
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