RHO (Ji et al., 2023a)

FLEEK (Bayat et al., 2023)

THAM Framework (Yoon et al., 2022)

Loss Weighting Method (Qiu et al., 2023b)

Knowledge Injection and Teacher-Student Approach (Elaraby et al., 2023)

Fine-tuning Language Models for Factuality (Tian et al., 2023)

HAR (Köksal et al., 2023)

BEINFO (Razumovskaia et al., 2023)

R-Tuning (Zhang et al., 2023a)
TWEAK (Qiu et al., 2023a)

Figure 1: Taxonomy of hallucination mitigation techniques in LLMs, focusing on prevalent methods that involve model
development and prompting techniques. Model development branches into various approaches, including new decoding
strategies, knowledge graph-based optimizations, the addition of novel loss function components, and supervised fine-tuning.
Meanwhile, prompt engineering can involve retrieval augmentation-based methods, feedback-based strategies, or prompt tuning.
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels