MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

• URL: http://arxiv.org/abs/2410.11779v1
• Date: Tue, 15 Oct 2024 16:57:44 GMT
• Title: MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
• Authors: Chenxi Wang, Xiang Chen, Ningyu Zhang, Bozhong Tian, Haoming Xu, Shumin Deng, Huajun Chen,
• Abstract summary: Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena. We propose a novel dynamic correction decoding method for MLLMs (DeCo) We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines.
• Score: 50.73561815838431
• License:
• Abstract: Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the objects in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs (DeCo), which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that DeCo is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations. Code is available at https://github.com/zjunlp/DeCo.

Related papers

• VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding [38.23310445372371]
  Large Vision-Language Models (LVLMs) have demonstrated outstanding performance in multimodal task reasoning. We propose a novel hallucination-mitigation method from the visual encoding perspective: textbfVisutextbfal textbfLayer Fustextbfion Contrastive textbfDecoding (VaLiD)
  arXiv  Detail & Related papers  (2024-11-24T13:42:02Z)
• DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language Models Meet False Premises [28.72485319617863]
  We propose a new prompting algorithm, named DecoPrompt, to mitigate hallucination. DecoPrompt leverages LLMs to "decode" the false-premise prompts without really eliciting hallucination output from LLMs. We perform experiments on two datasets, demonstrating that DecoPrompt can reduce hallucinations effectively on outputs from different LLMs.
  arXiv  Detail & Related papers  (2024-11-12T00:48:01Z)
• Lower Layer Matters: Alleviating Hallucination via Multi-Layer Fusion Contrastive Decoding with Truthfulness Refocused [44.37155553647802]
  Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks. They occasionally yield content that factually inaccurate or discordant with the expected output. Recent works have investigated contrastive decoding between the original model and an amateur model with induced hallucination. We introduce a novel contrastive decoding framework termed LOL (LOwer Layer Matters)
  arXiv  Detail & Related papers  (2024-08-16T14:23:59Z)
• MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification [1.3654846342364308]
  We introduce MetaToken, a lightweight binary classifier to detect hallucinations on the token-level at negligible cost. Based on a statistical analysis, we reveal key factors of hallucinations in LVLMs which have been overseen in previous works. We evaluate our method on four state-of-the-art LVLMs demonstrating the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-05-29T15:28:42Z)
• Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding [36.81476620057058]
  Large Vision-Language Models (LVLMs) are susceptible to object hallucinations. Current approaches often rely on the model's token likelihoods or other internal information. We introduce our CLIP-Guided Decoding approach to reduce object hallucination at decoding time.
  arXiv  Detail & Related papers  (2024-02-23T12:57:16Z)
• Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences [80.54979242912944]
  This paper introduces Mementos, a new benchmark designed to assess MLLMs' sequential image reasoning abilities. We find that MLLMs struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects.
  arXiv  Detail & Related papers  (2024-01-19T07:10:13Z)
• OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation [124.9008419182485]
  We present OPERA, a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns in the self-attention matrix. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue.
  arXiv  Detail & Related papers  (2023-11-29T18:57:07Z)
• Analyzing and Mitigating Object Hallucination in Large Vision-Language Models [110.12460299261531]
  Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. We propose a powerful algorithm, LVLM Hallucination Revisor (LURE), to rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions.
  arXiv  Detail & Related papers  (2023-10-01T18:10:53Z)
• DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models [79.01926242857613]
  Large language models (LLMs) are prone to hallucinations, generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts.
  arXiv  Detail & Related papers  (2023-09-07T17:45:31Z)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.