Hallucination in a foundation model (FM) refers to the generation of content
that strays from factual reality or includes fabricated information. This
survey paper provides an extensive overview of recent efforts that aim to
identify, elucidate, and tackle the problem of hallucination, with a particular
focus on ``Large'' Foundation Models (LFMs). The paper classifies various types
of hallucination phenomena that are specific to LFMs and establishes evaluation
criteria for assessing the extent of hallucination. It also examines existing
strategies for mitigating hallucination in LFMs and discusses potential
directions for future research in this area. Essentially, the paper offers a
comprehensive examination of the challenges and solutions related to
hallucination in LFMs.

在这篇综述论文中，研究了大型基础模型（LFMs）中幻觉问题的最新进展，包括幻觉现象的分类、评估标准以及减轻幻觉的策略和未来研究方向。

大型基础模型中的幻觉调查

A Survey of Hallucination in Large Foundation Models

Machine translation (MT) technology has facilitated our daily tasks by
providing accessible shortcuts for gathering, elaborating and communicating
information. However, it can suffer from biases that harm users and society at
large. As a relatively new field of inquiry, gender bias in MT still lacks
internal cohesion, which advocates for a unified framework to ease future
research. To this end, we: i) critically review current conceptualizations of
bias in light of theoretical insights from related disciplines, ii) summarize
previous analyses aimed at assessing gender bias in MT, iii) discuss the
mitigating strategies proposed so far, and iv) point toward potential
directions for future work.

通过批判性地审视当前有关 MT 偏见的概念化，总结以往的性别偏见分析，讨论已提出的缓解策略，并指向未来工作的潜在方向，提出了一个统一的框架以便于未来研究。