title: TruthPrInt: Mitigating LVLM Object Hallucination Via Latent
    Truthful-Guided Pre-Intervention
summary: Object Hallucination (OH) has been acknowledged as one of the major
  trustworthy challenges in Large Vision-Language Models (LVLMs). Recent
  advancements in Large Language Models (LLMs) indicate that internal states,
  such as hidden states, encode the "overall truthfulness" of generated
  responses. However, it remains under-explored how internal states in LVLMs
  function and whether they could serve as "per-token" hallucination indicators,
  which is essential for mitigating OH. In this paper, we first conduct an
  in-depth exploration of LVLM internal states in relation to OH issues and
  discover that (1) LVLM internal states are high-specificity per-token
  indicators of hallucination behaviors. Moreover, (2) different LVLMs encode
  universal patterns of hallucinations in common latent subspaces, indicating
  that there exist "generic truthful directions" shared by various LVLMs. Based
  on these discoveries, we propose Truthful-Guided Pre-Intervention (TruthPrInt)
  that first learns the truthful direction of LVLM decoding and then applies
  truthful-guided inference-time intervention during LVLM decoding. We further
  propose ComnHallu to enhance both cross-LVLM and cross-data hallucination
  detection transferability by constructing and aligning hallucination latent
  subspaces. We evaluate TruthPrInt in extensive experimental settings, including
  in-domain and out-of-domain scenarios, over popular LVLMs and OH benchmarks.
  Experimental results indicate that TruthPrInt significantly outperforms
  state-of-the-art methods. Codes will be available at
  https://github.com/jinhaoduan/TruthPrInt.
paper_authors._id: 67d42fbcf00a232c91a7a6c2
paper_authors.name: Jinhao Duan
paper_authors._id: 67d42fbcf00a232c91a7a6c3
paper_authors.name: Fei Kong
paper_authors._id: 67d42fbcf00a232c91a7a6c4
paper_authors.name: Hao Cheng
paper_authors._id: 67d42fbcf00a232c91a7a6c5
paper_authors.name: James Diffenderfer
paper_authors._id: 67d42fbcf00a232c91a7a6c6
paper_authors.name: Bhavya Kailkhura
paper_authors._id: 67d42fbcf00a232c91a7a6c7
paper_authors.name: Lichao Sun
paper_authors._id: 67d42fbcf00a232c91a7a6c8
paper_authors.name: Xiaofeng Zhu
paper_authors._id: 67d42fbcf00a232c91a7a6c9
paper_authors.name: Xiaoshuang Shi
paper_authors._id: 67d42fbcf00a232c91a7a6ca
paper_authors.name: Kaidi Xu
paper_title: TruthPrInt: Mitigating LVLM Object Hallucination Via Latent
    Truthful-Guided Pre-Intervention
paper_summary: Object Hallucination (OH) has been acknowledged as one of the major
  trustworthy challenges in Large Vision-Language Models (LVLMs). Recent
  advancements in Large Language Models (LLMs) indicate that internal states,
  such as hidden states, encode the "overall truthfulness" of generated
  responses. However, it remains under-explored how internal states in LVLMs
  function and whether they could serve as "per-token" hallucination indicators,
  which is essential for mitigating OH. In this paper, we first conduct an
  in-depth exploration of LVLM internal states in relation to OH issues and
  discover that (1) LVLM internal states are high-specificity per-token
  indicators of hallucination behaviors. Moreover, (2) different LVLMs encode
  universal patterns of hallucinations in common latent subspaces, indicating
  that there exist "generic truthful directions" shared by various LVLMs. Based
  on these discoveries, we propose Truthful-Guided Pre-Intervention (TruthPrInt)
  that first learns the truthful direction of LVLM decoding and then applies
  truthful-guided inference-time intervention during LVLM decoding. We further
  propose ComnHallu to enhance both cross-LVLM and cross-data hallucination
  detection transferability by constructing and aligning hallucination latent
  subspaces. We evaluate TruthPrInt in extensive experimental settings, including
  in-domain and out-of-domain scenarios, over popular LVLMs and OH benchmarks.
  Experimental results indicate that TruthPrInt significantly outperforms
  state-of-the-art methods. Codes will be available at
  https://github.com/jinhaoduan/TruthPrInt.
paper_ai_summary: Internal states in Large Vision-Language Models serve as per-token indicators of hallucination, allowing for the development of methods to mitigate object hallucination.
paper_ai_keywords: Object Hallucination (OH) Large Vision-Language Models (LVLMs) hidden states internal states per-token hallucination indicators truthful direction Truthful-Guided Pre-Intervention (TruthPrInt) ComnHallu hallucination latent subspaces