Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …

The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

With the advancement of language models (LMs), their exposure to private data is
increasingly inevitable, and their deployment (especially for smaller ones) on personal …

We investigate a surprising limitation of LLMs: their inability to consistently generate text in a
user's desired language. We create the Language Confusion Benchmark (LCB) to evaluate …

Large Vision-Language Models (LVLMs) are increasingly adept at generating contextually
detailed and coherent responses from visual inputs. However, their application in …

Large language models (LLMs) demonstrate remarkable multilingual capabilities without
being pre-trained on specially curated multilingual parallel corpora. It remains a challenging …

We propose a new framework that automatically generates high-quality segmentation masks
with their referring expressions as pseudo supervisions for referring image segmentation …

Zero-shot In-context learning is the phenomenon where models can perform the task simply
given the instructions. However, pre-trained large language models are known to be poorly …

Hallucinated translations pose significant threats and safety concerns when it comes to the
practical deployment of machine translation systems. Previous research works have …

Large Language Models (LLMs) demonstrate impressive performance in diverse
applications, yet they face significant drawbacks, including high inference latency …