Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs)
are plagued by the critical issue of hallucination. The reliable detection of such …

Large language models are known to hallucinate when faced with unfamiliar queries, but
the underlying mechanism that govern how models hallucinate are not yet fully understood …

Hallucination is a significant barrier to the utilization of large-scale language models or
multimodal models. In this study, we collected 654 computer science papers with" …

The surge in applications of large language models (LLMs) has prompted concerns about
the generation of misleading or fabricated information, known as hallucinations. Therefore …

Large Language Models (LLMs) have exhibited remarkable performance on reasoning
tasks. They utilize autoregressive token generation to construct reasoning trajectories …

Despite their remarkable capabilities, Large Language Models (LLMs) are prone to
generate responses that contradict verifiable facts, ie, unfaithful hallucination content …

In the medical domain, numerous scenarios necessitate the long-form generation ability of
large language models (LLMs). Specifically, when addressing patients' questions, it is …

As AI systems become more capable, widely deployed, and increasingly autonomous in
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …

Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across
various vision-language tasks. However, they suffer from visual hallucination, where the …

While large language models are rapidly moving towards consumer-facing applications,
they are often still prone to factual errors and hallucinations. In order to reduce the potential …