Hallucination
684 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Hallucination
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Libraries
Use these libraries to find Hallucination models and implementations
MMNeedle
Most implemented papers
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
ReAct: Synergizing Reasoning and Acting in Language Models
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e. g. chain-of-thought prompting) and acting (e. g. action plan generation) have primarily been studied as separate topics.
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Large Vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned.
Evaluating Object Hallucination in Large Vision-Language Models
rucaibox/pope
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Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i. e. they tend to generate objects that are inconsistent with the target images in the descriptions.
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
openbmb/omnilmm
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Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition.
Pushing the Limits of Low-Resource Morphological Inflection
Recent years have seen exceptional strides in the task of automatic morphological inflection generation.
Projected Distribution Loss for Image Enhancement
saurabh-kataria/projected-distribution-loss
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More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts.
