Hallucination has been widely recognized to be a significant drawback for large language
models (LLMs). There have been many works that attempt to reduce the extent of …

This paper presents a systematic defense of large language model (LLM) hallucinations
or'confabulations' as a potential resource instead of a categorically negative pitfall. The …

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" …

Hallucination detection is a challenging task for large language models (LLMs), and existing
studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we …

Background Although discharge summaries in patient-friendly language can enhance
patient comprehension and satisfaction, they can also increase medical staff workload …

Large Language Models (LLMs) are highly capable but require significant computational
resources for both training and inference. Within the LLM family, smaller models (those with …

Hallucination is a common problem for Large Vision-Language Models (LVLMs) with long
generations which is difficult to eradicate. The generation with hallucinations is partially …

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 …

Internet technology has given rise to an open-collaborative software development paradigm,
necessitating the open-collaborative schema to software testing. It enables diverse and …

Large language models (LLMs) often suffer from context faithfulness hallucinations, where
outputs deviate from retrieved information due to insufficient context utilization and high …