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cs.CL2024
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations
Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal +5
Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowle…
cs.CV2024
Tackling Structural Hallucination in Image Translation with Local Diffusion
Seunghoi Kim, Chen Jin, Tom Diethe +5
Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in…
cs.CV2024
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran +2
Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural l…