We propose a self-correction mechanism for Large Language Models (LLMs) to
mitigate issues such as toxicity and fact hallucination. This method involves
refining model outputs through an ensemble of critics and the model's own
feedback. Drawing inspiration from human behavior, we explore whether LLMs can
emulate the self-correction process observed in humans who often engage in
self-reflection and seek input from others to refine their understanding of
complex topics. Our approach is model-agnostic and can be applied across
various domains to enhance trustworthiness by addressing fairness, bias, and
robustness concerns. We consistently observe performance improvements in LLMs
for reducing toxicity and correcting factual errors.

提出了一种用于改善大型语言模型的自我纠正机制，通过批评家与模型自己的反馈对模型输出进行精炼，以减轻毒性和事实幻觉等问题。通过人类行为的启发，探讨了大型语言模型是否可以模仿人类的自我纠正过程，即借助自评和寻求他人意见来完善对复杂主题的理解。该方法与特定模型无关，可应用于各个领域，通过解决公平性、偏见和鲁棒性问题，提高可信度。我们始终观察到大型语言模型在减少毒性和纠正事实错误方面的性能改进。

N-Critics: 大型语言模型的自我提升与评论家集成

N-Critics: Self-Refinement of Large Language Models with Ensemble of  Critics

Knowledge Graph(KG) grounded conversations often use large pre-trained models
and usually suffer from fact hallucination. Frequently entities with no
references in knowledge sources and conversation history are introduced into
responses, thus hindering the flow of the conversation -- existing work attempt
to overcome this issue by tweaking the training procedure or using a multi-step
refining method. However, minimal effort is put into constructing an
entity-level hallucination detection system, which would provide fine-grained
signals that control fallacious content while generating responses. As a first
step to address this issue, we dive deep to identify various modes of
hallucination in KG-grounded chatbots through human feedback analysis.
Secondly, we propose a series of perturbation strategies to create a synthetic
dataset named FADE (FActual Dialogue Hallucination DEtection Dataset). Finally,
we conduct comprehensive data analyses and create multiple baseline models for
hallucination detection to compare against human-verified data and already
established benchmarks.

该研究通过人工反馈分析，提出了一种针对知识图谱 (Knowledge Graph) 聊天机器人中存在的事实幻觉问题的实体级幻觉检测系统，并创建了 FADE 合成数据集，用于与已建立的基准进行比较。