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Hallucination

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Showing 876–900 of 1816 papers

TitleStatusHype
LLM Internal States Reveal Hallucination Risk Faced With a QueryCode0
FSM: A Finite State Machine Based Zero-Shot Prompting Paradigm for Multi-Hop Question Answering—0
MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical ContextCode1
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification—0
Understanding Alignment in Multimodal LLMs: A Comprehensive Study—0
MeMemo: On-device Retrieval Augmentation for Private and Personalized Text GenerationCode2
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem—0
LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation—0
Free-text Rationale Generation under Readability Level Control—0
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak AttacksCode0
FineSurE: Fine-grained Summarization Evaluation using LLMsCode1
Unveiling Glitches: A Deep Dive into Image Encoding Bugs within CLIP—0
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language ModelsCode1
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical ScienceCode0
GraphArena: Benchmarking Large Language Models on Graph Computational ProblemsCode1
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models—0
A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model—0
Applying RLAIF for Code Generation with API-usage in Lightweight LLMs—0
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language ModelsCode1
Handling Ontology Gaps in Semantic ParsingCode0
From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic DataCode0
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented GenerationCode2
Mitigating Hallucination in Fictional Character Role-PlayCode0
Evaluating the Quality of Hallucination Benchmarks for Large Vision-Language ModelsCode1
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