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Hallucination

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Showing 1001–1050 of 1816 papers

TitleStatusHype
ESREAL: Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models—0
Explore the Hallucination on Low-level Perception for MLLMs—0
Exploring and Evaluating Hallucinations in LLM-Powered Code Generation—0
Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models—0
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection—0
Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization—0
Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models—0
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness—0
Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges—0
FaceDet3D: Facial Expressions with 3D Geometric Detail Hallucination—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
Face Hallucination Revisited: An Exploratory Study on Dataset Bias—0
Face hallucination using cascaded super-resolution and identity priors—0
Face Hallucination using Linear Models of Coupled Sparse Support—0
Face Hallucination with Finishing Touches—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Facial Attribute Capsules for Noise Face Super Resolution—0
Facial Feature Point Detection: A Comprehensive Survey—0
Facial Reenactment Through a Personalized Generator—0
FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation—0
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification—0
FactCheckmate: Preemptively Detecting and Mitigating Hallucinations in LMs—0
FACTOID: FACtual enTailment fOr hallucInation Detection—0
FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs—0
Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales—0
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities—0
Feature Hallucination for Self-supervised Action Recognition—0
Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation—0
Fewer Truncations Improve Language Modeling—0
Few-Shot Learning via Saliency-guided Hallucination of Samples—0
Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems—0
FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping—0
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback—0
FH-GAN: Face Hallucination and Recognition using Generative Adversarial Network—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Fine-grained Hallucination Detection and Editing for Language Models—0
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning—0
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction—0
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy—0
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction—0
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Flow Dynamics Correction for Action Recognition—0
FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt—0
Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors—0
Foundations of GenIR—0
Foundations of Unknown-aware Machine Learning—0
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs—0
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