MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Authors: Chenxi Wang
Xiang Chen
Ningyu Zhang
Bozhong Tian
Haoming Xu
Shumin Deng
Huajun Chen
Venue: ICLR 2025 Poster (2025)
Keywords: Hallucination Mitigation
Multimodal Large Language Models
Decoding Strategy
Primary area: foundation or frontier models, including LLMs
TL;DR: A 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 for hallucination mitigation.

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.

Published as a conference paper at ICLR 2025
MLLM CAN SEE? DYNAMIC CORRECTION DECODING FOR HALLUCINATION MITIGATION
Chenxi Wang Xiang Chen 9; Ningyu Zhang i Buzhong Tian Haoming Xu
Shumin Deng Huajun Chen
Zhejiang University Nanjing University of Aeronautics and Astronautics
National University of Singapore, NUS-NCS Joint Lab, Singapore
Zhejiang Key Laboratory of Big Data Intelligent Computing s sunnywex, zhangningyu ju edu. cn, xiang.chen@nuaa. edu. cn
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 gen- erate the objects in the final output, they are actually able to recognize visual objects the preceding layers. We speculate that this may be due the strong knowledge priors of the language model suppressing the visual information, lead- ing hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MILMs (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 seam- lessly incorporated with various classic decoding strategics and applied to dif- ferent MILMs. 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 ballucinations
"The first principle is that you must not fool yourself and you are the easiest person to fool."
-Richard Feynman
INTRODUCTION
Recently, the rapid development of Multimodal Large Language Models (MLLMs) has demon- strated a potential pathway towards achieving Artificial General Intelligence (AGI) (Wang et al., 2024b; Yao et al., 2024; Lu et al., 2024a; Team, 2024; OpenAI, 2023; Liu al., 2023b; Chern al., 2024). However, in practice, the development of MILMs is hindered by the phenomenon halluci- nation, which typically results the model generating statements about non-existent images while neglecting to mention certain visible objects, effectively causing it to fool itself (Bai et al., 2024; Liu et al., 2024a; Li et al., 2023b; Liu et al., 2023a; Rawte al., 2023). This issue poses significant risks in high-stakes fields such as medical imaging (Chen et al., 2024b; Hu et al., 2023; Wang et al., 2023c), autonomous driving (Cui et al., 2024; Wang et al., 2023d), and human computer interaction systems (Brie et al., 2023), where such errors could result in irreparable consequences.
The reasons behind hallucinations in MILMSs are complex. Unlike analyses focused on unimodal LLMSs (Chuang et al., 2024; Chen et al., 2024d; Orgad et al., 2024; Chen et al., 2024e; Lu et al., 2024b; Wang et al., 2024a), many current works assume that MLLM may indeed "see" visual infor- mation. However, due factors such as excessive model depth (Chen et al., 2024c; Zhang et al., 2024a), aggregation patterns (Huang et al., 2024), or priors knowledge inherent in the MLLMS (Leng et al., 2023; Zhang et al., 2024b), these models ultimately still experience hallucinations (The details can be found in Appendix B). Concretely, our understanding of the underlying mechanisms of hal- lucinations in MILMS remains limited. It is still uncertain whether the visual information is never correctly recognized or if it is recognized but subsequently suppressed by later information streams.
Equal Contribution.
1 Corresponding Author.
Code available https: / / httg//tttub..coo http:////gggttbbb.oo////////////

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Figure 1: Overall results the probing experiment with MLLMs, indicating that they possess a certain level of awareness regarding the presence of visual objects (Figure (()), with prediction accuracy being higher in the preceding layers (Figure ((b) but gradually decline afterward.
Hallucinated MLLM can see (to some extent). Inspired by the aforementioned works, we conduct an empirical analysis and find that MLLMs are not blind; they can recognize objects in the preceding layers, but this recognition is suppressed in later layers, leading to hallucinations. Specifically, we focus on object hallucinations" and conduct experiments with MILMs, demonstrating that they know to some extent whether object exists (as shown in Figure 1 and Section 2.1). We further observe that the confidence generated tokens is influenced by the knowledge priors of MLLMs (Section 2.2), leading to reduction in the probability of ground truth tokens in the deeper layers.
Dynamic correction decoding with preceding-laye knowledge. Based on those findings, we pro- pose Dynamic Correction Decoding with preCoding-Layer Knowledge (DeCo) to mitigate hallu- cinations for MILMs. Our core hypothesis that preceding layers exhibit higher confidence for ground truth tokens, and the logits for these tokens should rank prominently the last layer's out- puts. To enhance the logits of ground truth tokens, DeCo dynamically selects preceding layer and utilizes its prior knowledge to correct the final output logits. Additionally, we introduce dynamic soft modulation to preserve the original style of the generated responses. DeCo is training-free and can be integrated with any popular decoding strategies, such greedy search, nucleus sampling as well as beam search, and can seamlessly incorporate into any MLLMs for hallucination mitigation.
Contributions. Our primary contribution lies exploring the internal mechanisms of hallucinations in MLLMs. We find that the confidence of generated tokens is influenced by the knowledge priors of MLLMs, leading to reduction in the probability of ground truth tokens in the deeper layers. We further propose DeCo, dynamic correction decoding method guided by preceding-late edge. DeCo integrated with InstructBLIP, MiniGPT-4, LLaVA, and Qwen- VL using three popular decoding strategies: greedy search, nucleus sampling, and beam search. Experimental results show that DeCo achieves an average hallucination suppression rate of 10.8% in image captioning dataset, demonstrating superior suppression effectiveness. Additionally, DeCo outperforms basclines vi- sual question answering datasets including POPE, and MME. Additionally, we analyze the latency and throughput, showing that DeCo introduces approximate 1.2x increase in latency compared to the basic decoding process, much faster than previous baselines such as VCD and OPERA.
knowl-
WHY DO MLLMS GENERATE NON-EXIST OBJECTS?
In this section, we conduct a series of empirical analysis to investigate the internal mechanisms of MILMM and elucidate the underlying reasons for its generation of non-existent objects. To strike balance between the realism and complexity of the experiments, we primarily focus on the genera- tion of objects in image description scenarios (image caption tasks).
Preliminaries of MILMM generation. MILMs typically concatenate visual tokens, processed by the visual encoder and projection layer, with embedded textual tokens before feeding them into an autoregressive language model. We denote the visual tokens as XV { Tegr ... Tup and textual tokens as X {Ie Kegy Teq. Here P and Q are the lengths of the visual tokens
This approach is applicable other types of hallucinations as well.
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Figure 2: Illustration of token probabilities across transformer layers, which reveals distinct trends for target hallucinated (orange) and non-hallucinated (green) tokens. In the preceding layers, non- hallucinated tokens exhibit higher probability. In the final layers, hallucinated tokens demonstrate increased probabilities, while the probability of non-hallucinated tokens drops sharply.
and textual tokens respectively. Finally, the input is X concat .x Then X would be passed into MI.LM with N stacked transformer layer. The intermediate variable generated by the i-th layer is called hidden states, denoted h' {hg -i....; - where T = P + Q. During the generation phase, we use the hidden state at the last position in the final layer, which is mapped
the vocabulary dimension through an affine layer ((-), to predict the probability of the next token. Formally, we have:
p(zT|e<r) = softmax (oot ())) 2T V
(1)
where we use Fęr simplify the sequence {2r) (T)) and V refers to the whole vocabulary set.
2.1 FINDING 1: MLLM KNOWS TO SOME EXTENT WHETHER AN OBJECT EXISTS
Inspired by (Ye et al., 2024), we explore how MILMs comprehend objects in the image captioning task. For simplicity, whether an object is present in an image. To examine the application of this function within the MLLM's image captioning workflow, conduct probing experiments at the conclusion of object deseriptions in each layer of the MLLM's language model component, which consists of 32 trans- former layers in 7-billion-parameter model (Detailed setup in Appendix C.I).
abstract this process into function called isexist(obj). which determines
We employ the prompt template, "USER: <image>Deen Describe the image. ASSISTANT: The image contains obj." " Both the training and testing datasets formatted accordingly before being input into MLLMs. We train a probe classifier at the final position of the hidden state outputs for each transformer layer, resulting
total of 32 classifiers. (For details on the subset division, OOD and in-distribution splits, and prompt templates, please refer to Appendix C.I.) The model is evaluated using the test set, as shown in Figure (a)) (left). Further experiments are conducted on three splits of the evaluation dataset proposed by POPE, with results reported in Figure 1(a) (right). These evaluations provide a comprehensive understanding of the model's object recognition capabilities across diverse scenarios.
We select the best-performing probe classifier from the 32 classifiers to compare accuracy across all objects, existing objects, and non-existing objects. Our results show that the MLLM achieves high accuracy for correctly generated objects in image captions. Despite generating many non non-cxistent objects, the MILM still maintains around 80% accuracy in our probing experiments. This suggests that MLLMs possess a certain level of understanding regarding object existence in images.
Additionally, our probing experiments reveal higher accuracy in the preceding layers, illustrated in Figure (bb, which aligns with previous findings (Zhang et al., 2024b; Leng et al., 2023). Further- more, show that increasing the resolution of the visual encoder (from 224px 336px) enhances
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accuracy for non-existing objects, indicating that token information at the last position in the preceding layers better represents visual information. (For a detailed explanation of the different visual resolutions, please refer to Appendix C.I). These findings suggests that the utilization of the preceding-layee in MI.LMs enables the model to perform self-correction.
2.2 FINDING 2: LANGUAGE MODEL PRIORS SUPPRESS THE VISUAL INFORMATION THAT MLLM ALREADY SEE.
We hypothesize that the representations in the preceding layers effectively capture (to some extent) visual information. However, the prior knowledge embedded in the MILM reduces the probabilities of ground truth tokens deeper layers. Figure 2 illustrates this hypothesis with running examples. We analyze the T tokens ranked by probability in the final layer's s output. Non-hallucinated to- kens like "people". "left", "blue", and "umbrella" " exhibit high probabilities from the 18th layer. In contrast, hallucinated tokens like "bird" and "green" only show comparatively high probabili- ties around the 30-th layer. Interestingly. the probabilities of ground truth tokens "umbrella" " and "blue" sharply decline from the 30-th layer onwards, eventually falling below the hallucinated kens' probabilities in the final layer.
To further investigate this phenomenon, we conduct an early exit experiment (Teerapittayanon et al., 2016; Elbayad et al.,
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2020; Schuster et al., 2022) to analyze the evolution of the MLLM's $ internal representations across transformer layers. We randomly select 500 images from the MSOCO dataset and use random prompts to elicit raw responses from LLaVA- 1.5-7b. We then extract all non-existent objects along with their corresponding preceding text and input this data into the MLLM. We observe the probabilities of the next token across the transformer layers to gain insights into the model's behav- ior (see Appendix C.2 for detailed experimental setup). The output of the i-th layer is denoted as h', and the probability distribution of the next token is represented as p(-(==<))* softmax(((hi-1))) (o(hi-1)) To reduce the obser- vation tokens and simulate the real sampling process, we truneate the vocabulary, similar Top-p sampling, and obtain the candidate tokens, denoted as Veandidate with a default threshold of 0.9. We then label the tokens in Voandidate- Specifically, we filter out data where Voandidate contains at least one ground truth token and observe whether activated ground truth token exists among the candidate tokens, formally expressed as:
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Figure 3: Distribution of activated ground-truth tokens across layers.
softmax
3 F. E Veandidate A E NJ, p(zal=ee) p( threshold,
where xa is the activated ground truth token, 3h is the token with the highest probability of being hallucinated token in the probability distribution of the final layer and threshold e (0, 1). Based on the experimental setup described above, we conducted the following investigation:
What suppresses the expression of visual facts? We analyze the occurrence of x, at each decod- ing layer, as shown in Figure 3. The results reveal that the activated ground truth tokens are primarily present between layers 20 and 28, indicating that MLLMs accurately recognize the image content in the latter layers. Notably, differences in experimental setups account for the variation in interval layers observed between Finding and Finding 2. However, the activated ground truth tokens suppressed in the final output layer. This suppression may stem from the guidance of the input im- age or the inherent knowledge bias of the MILM. To investigate this, generate candidate tokens condidate in the absence of an input image, representing tokens based on the MLLM's 's inherent knowledge. We calculate that the overlap rate F₁ existing in V V'andiiatte reaches 91.05%, sug- gesting that without expressing image information, MLLMs still tend to generate the original hallucination tokens. This finding reveals that the inherent knowledge in MLLMs may diminish the probability of the ground truth token in the deeper layers.
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Figure 4: Framework of DeCo. DeCo first dynamically selects an appropriate anchor layer from the preceding layers and then correct the knowledge in the final layer with dynamic coefficient.
PROPOSED APPROACH: DYNAMIC CORRECTION DECODING WITH PRECEDING-LAYER KNOWLEDGE
After investigating the reasons why MLLMs generate non-existent objects, inspired by (Chuang et al., 2024), we introduce Dynamic Correction Decoding with preCeding-Layer Knowledge (DeCo), which can alleviate hallucinations during inference. The overall framework Deco is illustrated in Figure 4, consisting of dynamic preceding layer selection (Section 3.1) and decoding correction with preceding- knowledge (Section 3.2).
3.1 DYNAMIC PRECEDING-LAVER SELECTION
Candidate token acquisition. Due to the vast vocabulary space, we track only the changes in the top-ranked tokens as candidate tokens across different layers for computational convenience. This is based on the hypothesis that ground tokens usually appear in the top position of the MLLM's last layer output logits. Inspired by (Li al., 2023a), use truncation strategy select the candidate tokens, with the default truncation strategy being top-p truncation, formally:
Veandidate (IT(EET)) Ir EV: Σ Pt = u/ Ft, Fi,-..,TTT-II....
FT .T
Sp
VEVp
where is is the whole vocabulary, and refers to the parameter used in top-p. The selected candidate tokens theoretically ensured to be of high quality, thereby preventing the inclusion of low quality tokens semantically incorrect tokens) that exhibit high probabilities in preceding-layers but low probabilities the final layer.
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Preceding-layer selection. Our findings in Section demonstrate that activated ground truth tokens typically ex- hibit higher probabilities in preceding layers compared to hal- lucinated tokens. Based on this observation, we hypothesize that selecting the token Eih,, where Feh Veandidate- with the highest probability from the interval layers corresponds to the ground truth token. We compute the accuracy of 3thh as the ground truth token and denote this metric as the hit rate, as shown in Table 1. The results indicate that within specific range of layers (e.g.. 15-28), 2th indeed has a high universal probability of rep- resenting the ground truth token. Intuitively, we track candidate tokens and dynamically choose the layer in which the token with the highest probability among the preceding layers resides to calibrate the final logit distribution of the MILM. The selected preceding layer is referred to as the anchor layer, formally defined as:
Table I: Hit Rate of layers across different intervals.
Layer Range
20-28
15-28
Hit Rate (%)
61.69
71.14
A = argmax, {IT E Veandidate (oftmax(o(hh)-)))
[a, b)
(4)
where S b, a, b [1, NJ. and [a, b] represents the layer interval for MLLMs. Expanding the range of layers can improve the hit rate. To avoid increased search computation time, we assign default values of 20 and b 28 for our subsequent experiments.
3.2 DECODING CORRECTION WITH PRECEDING-LAVER KNOWLEDGE
Dynamic soft modulation. We introduce dynamic modulation coefficciet,, defaulting the max- imum probability. Formally, we have:
max prob = (ox(softmaxx(((h:)11))
(5)
This coefficient can help prevent hard changes in logits, particularly when the probability differences between candidate tokens in preceding layers insignificant. From the example Figure can observe that the absence of the dynamic modulation coefficient may lead to semantic incoherence or even more severe hallucinations.
Preceding-layer knowledge guided decoding. Given the selected preceding layers, integrate information from these layers into the final layer to correct the logit distribution. We utilze hyperpa- rameter, a, control the proportion of early-layer information incorporated. Additionally, dynamic soft modulation is employed to preserve the generative style of the original model. By utilizing the correction of preceding-laye layer representations, the probability of predicting the next token and the logits are updated as follows:
p(ar T<T) = softmax (logits) øT
(6)
logits = <(h + a x max x ((
(7)
where is the last layer MLLM and A is the selected preceding layer.
4 EXPERIMENT
4.1 SETUP
Baselines. We integrate DeCo with various decoding methods, including greedy decoding, nucleus sampling, and beam search, and compare it against several basclines for mitigating hallucinations, as outlined below: Dola (Chuang et al., 2024) specifically designed for alleviating hallucinations in factual tasks for LLMs by reducing shallow semantic influences to improve the factuality of the final layer's s output. VCD (Leng et al., 2023) mitigates the influence language model's priors in MLLMs by generating representations that enhance visual information through the subtraction of interfering knowledge prior during each sampling step. OPERA (Huang et al., 2024) dynamically penalizes overconfident tokens based on the emergence of aggregation patterns, while proposing retrospective allocation strategy to avoid cases where hallucinations have already occurred. For all the baselines, the default hyperparameters from the source code for fair comparsion.

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Table 2: CHAIR hallucination evaluation results. Lower scores indicate fewer hallucinations. OPERA utilizes beam search, VCD applies nucleus sampling, and DeCo is the proposed method compatible with various decoding approaches.
InstructBLII
MiniGPT-4
LLaVA-13
Qwen-VL
Decoding
Method
CHAIRS
CHAIR;
CHAIRS
CHAIR
CHAIRS
CHAIR;
CHAIRS
CHAIR;
Greedy
Vanilla
58.8
31.8
9.9
45.0
46.0
12.5
DoLa
48.4
15.9
32.2
10.0
47.8
13.8
46,8
12.9
DeCo (Ours)
41.2
14.4.
27.0 (48
8.8
37.8 (7.2
11.1 .366
42.2 138
10.7 .18
Vanilla
55.6
15.8
30.6
9.5
48.8
13.9
41.8
10.8
Beam Search
OPERA
46.4
14,2
26.2
9.5
44.6
12.8
34.6
9.5
DeCo (Ours)
43.8 111.8
12.7 .311
24.8 15.8
7.5 12.0
33.0 115.8
9.7 14.2
32,0 (2,8
8.7 12.1
Vanilla
54.6
24.8
32.6
10.7
48.8
14,2
49.2
13.1
Nucleus
VCD
58.0
33.8
54.0
46.4
DeCo (Ours)
43.6 111.0
12,9 .1.9
30.8
11.8
9.5 11.2
42.8 16.0
13.2 11.
43.8 15.4
11.8 113
Table 3: POPE hallucination evaluation results. The best results are in bold.
InstructBI.IP
MiniGPT4
LLaVA-155
Qwen-VI. F11
Decoding
F1
F11
F1
Vanilla
80.0
58.5
82.2
85.2
Greedy
DoLa
83.4
72.8
83.2
85.8
DeCo (Ours)
84.9 14.9
77.4 118.9
86.7 14.5
86.3 11.
Vanilla
84.4
70.3
84.9
85.3
Beam Search
OPERA
84.8
73.3
85.4
86.1
DeCo (Ours)
84.9 10.5
77.9 17.6
11.8
86.4
Vanilla
79.8
52.8
83.1
84.5
Nocleus
VCD
79.9
56.0
83.1
84.7
DeCo (Ours)
81.8
63.8
85.4 12.3
85.2
Model. We select four of the most representative MLLM models for evaluation, including Instruct- BLIP (Dai et al., 2023), MiniGPT-4 (Zhu et al., 2024), LLa (Liuu al., 2023b) and Qwen-VI. (Bai et al., 2023). All the MLLMs we used have language model size of 7 billion parameters (7B).
Implementation Details. To select the appropriate preceding layers for hallucination mitigation, we conduct ablation experiments, details of which can be found in the Section 4.4. For 7B-sized, 32-layer decoder-only architecture language model, we choose layers 20-28 as candidates for the preceding layers (according the findings in Section 2.1). For the image captioning and VQA tasks, a is set within the range of 0.1 to 0.6. In all experiments, we conduct inference on a single A800 GPU. The inference of 500 image-caption pairs take approximately 40 minutes.
4.2 BENCHMARK AND METRICS
CHAIR. Caption Hallucination Assessment with Image Relevance (CHAIR) (Rohrbach et al., 2018) metric, widely used in image captioning. identifies hallucinated objects by comparing the extracted objects with ground truth labels and evaluates both at the instance level (CHAIR) and sentence level (CHAIRs), as shown in Eq. 8. Following (Huang et al., 2024), we conduct experiments using the same settings, including the consistent 500 images from the MSCOCO 2014 validation dataset and the identical prompt, "Please help me describe the image in detail.".
CHAIR; (halluuiinated objects)| }
C.AIIRS [captions with hallucinated objects)|
all mentioned objects
all captions
POPE, The colling-based Object Probing Evaluation (POPE) (Li et al., 2023b) is a VQA-based metric for assessing object hallucination in MI.LMs. It evaluates hallucinations by asking questions such "Is there <object> in the image? ?" where <object>> is derived from three types of splits: random (randomly selected objects), popular (frequently occurring objects), and adversarial (objects closely related to those in the image). The evaluation includes 500 MSCOCO images, with six questions per image for each split. We use Fl score for performance evaluation.
The comprehensive MLLM Evaluation benchmark (MME) (Fu et al., 2023) assesses the perceptual and cognitive abilities of MLLMs across total of 14 subtasks, including tasks such as OCR, visual knowledge, attribute relationships, and object recognition.
MME.
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GPT-4o assisted evaluation. To further assess the model'ss performance in image captioning, tend beyond the CHAIR metric, which targets object hallucination. Following prior studies (Huang et al., 2024; Leng et al., 2023), open evaluation is conducted using GPT-4o 100 randomly sampled COCO images. GPT-4o assesses two assistants' deseriptions terms of Accuracy (A) (e.g., truthfulness), Detailedness (D) (e.g., richness) and Coherence (C). We introduce the prompt used in the experiments in Table 10 and Table 11.
4.3 EXPERIMENTAL RESULTS
Results of hallucination in image captioning, Note that we use the baseline's original decod- ing settings for fair comparison and run DeCo under the same settings. From Table 2, we no- tice that DeCo consistently outperforms other approaches in mitigating hallucinations across four MiniGPT-4, LLaVA-1.5, and Qwen-VL- VL--using three decoding strategies: greedy search, beam search, and nucleus sampling. We find that DeCo slightly outperforms OPERA, while our method demonstrates higher efficiency and simplicity in inference (see Section 4.4), Ad- ditionally, VCD does not perform as well, likely due to producing an increased number of halluci- nated descriptions during the generation process. In conclusion, the proposed approach DeCo effec- tively reduces hallucinations in visual description tasks solely through dynamic decoding correction, achieving average suppression rate of approximately 10.8 % image captioning datasets. Ad- ditionally, we further evaluate the performance of DeCo on the AMBER image caption dataset, as detailed in Table of the Appendix.
Results of hallucination in VQA. In contrast to image cap- tioning, POPE employs simple polling approach to assess hallucination levels in MLLMs with respect to object recog- nition. As shown in Table 3, DeCo demonstrates superior per- formance across all settings, further validating the effective- ness of the proposed approach. Additionally, Figure reveals that DeCo also achieves better results on MME, which evalu- ates the multifaceted VQA capabilities of LLaVA-1.5. These findings suggest that the underlying mechanism we identi- fied not only applies to object recognition but also extends to attribute-related tasks and more complex reasoning tasks.
Nac
VCD
Store
1250-
OPERA
Beam
1000
Wusieg
VDD
Bear
Data
Deca
Figure 5: DeCo generally improves the MLLM's performance.
Results of GPT-4o's assistance. Following (Huang et al., 2024; Leng et al., 2023), we further use GPT-4o to evaluate method against greedy decoding across four distinct models. From Table 4, we notice that our approach consistently outperform greedy decoding in terms of accuracy, demon- strating its efficacy in hallucination suppression. The impact of decoding intervention is evident in the level of detail produced: for some models, our method yield only marginally higher or, in certain cases, slightly lower levels of detail compared greedy decoding. DeCo also exhibits coherence level comparable to that of the baseline. Nonetheless, our method exhibit clear advantage in miti- gating hallucinations across all evaluated models.
Table 4: GPT-4o assisted hallucination evaluation results MSCOCO. Three aspects are verified, accuracy (A), detailedness (D) and coherence (C).
InstructBLIP
MiniGPT-4
LLaVA-1.5
Qwen- VL
Method
A
D
C
A
D
C
A
D
C
A
D
C
Greedy Search
4.92
5.65
6.89
5.71
6.20
7.67
5.21
6.31
8.18
5.56
6.62
8.20
DeCo (Ours)
6.25
5.77
7.14
6.33
6.08
7.54
7.42
6.25
7.96
7.81
6.70
8.15
4.4 ANALYSIS
Latency and throughput analysis. To evaluate the efficiency of DeCo, we compare its latency and throughput with several baselines, including DoLa, OPERA, and VCD based on Greedy, Beam Search, and Nucleus Sampling, respectively. Figure 6 illustrates the results of this compar- ison. The findings indicate that DeCo operates within an acceptable efficiency cost, striking a bal- ance between effectiveness and computational overhead. Compared the basic decoding process,
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the latency increase introduced by method is approximately 1.2 times. In contrast, the latency increases for VCD and OPERA are 1.8 and 5.1 times, respectively. While both VCD and OPERA demonstrate comparable efficacy in mitigating hallucinations, their computational overheads remain relatively high. This highlights the practical value of DeCo, as it can be integrated into real-world applications without significantly compromising efficiency.
Perturbation in the selected preceding-layer. To eval- uate the effectiveness of the dynamic layer selection method, we introduce a random perturbation strategy. Specifically, for the predetermined preceding layers, we add random values ranging from -5 to to modify the selection of layers. We randomly select 200 images from the MSCOCO dataset and prompt MLLMs to generate descriptions. The results after incorporating the perturbations are presented in Table 5. Notably, the perturbed results demonstrate a significant degradation in performance, further validating the effec- tiveness of our proposed method.
Figure 6: Comparison of latency and throughput across different baselines.
Table 5: Comparison of results between DeCo and perturbed DeCo in image captioning tasks
InstrueBLLIP
MmiGPT-4
LLaVA-1.5
Qwen-VL
Method
CHAIRs
CHAIR
CHAIRs L CHAIRI 4.
CHAIRs 4
CHAIR,
CHAIRs
CHAIR:
DeCo
39.3
12.6
32.4
9.6
38.8
IL1
44.5
11.1
DeCo 45.6 16.
117
33.3
10.9
10.1
12.4
11.3
10.2
225
117
Hyperparameter analysis. Our method incorporates two primary hyperparameters: a and the se- lection of interval layers. In the experiments, employ DeCo based on greedy decoding. On the one hand, the hyperparameter a regulates the intensity of early information enhancement. Figure 7(a) illustrates the performance across various values. We observe that hallucination suppression is most effective when a approximates 0.6. As a increases, the efficacy DeCo in mitigating hal- lucinations improves. However, it is crucial to note that excessively high values may lead to the generation of atypical image descriptions, characterized by repetitive word usage. Notably, we can adjust the value of alpha appropriately responses (e.g., by using lower alpha). Additionally, our approach and the hyperparameter for rep- etition penalty are orthogonal, which implies that we can introduce the repetition penalty term to mitigate repetition. On the other hand, the layer interval hyperparameter [a, b) determines the can- didate layers for inclusion the enhancement process. We conduct experiments using intervals of four layers, with results presented in Figure 7(b). Our analysis reveals that hallucination suppres- sion for MLLM is negligible in layers 1-16, while layers 20-28 demonstrate substantial mitigation of hallucinations. Notably, layers 29-32 exhibit minimal hallucination suppression, aligning with our findings discussed Section 2.2. For other families of MLLMs and larger scale MLLMs, the selection of interval layer should be appropriately adjusted based empirical experimentation.
balance the truthfulness and semantic coherence of the
LLaVA MinGPT-4
Owen-VL
InstructSLIP
OuT
Greedy
USA
MHGFT4
50
50
CHAIRS
40-
CHAIR,
40
35-
30
30-
25
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
14
588
9.12
25.28 29/32 Layer Interval
1336
17.20
21.24
Alpha
(a) Ablation study of a.
(b) Ablation study of interval layers.
Figure 7: Ablation experiment results for hyperparameter a and different interval layers.
Mitigating snowballing hallucinations. Snowballing hallucinations are a prevalent issue in the responses generated by MILMMs. This phenomenon occurs when initial hallucination triggers

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sequence of subsequent errors, leading to a compounding effect that significantly degrades the qual- ity and coherence of the generated text. Figure 8 illustrates a typical example of snowballing hallu- cinations, where initial misinterpretation of the visual input propagates through the decoding pro- cess, resulting in a highly inconsistent and erroneous output. Our approach can reduce the accumu- lation of errors and improves the overall consistency and accuracy of the generated responses. The effectiveness of DeCo is further demonstrated through additional cases based on diverse MLLMs, which can be found in Figures 10, 11, and 12 in Appendix G.
RELATED WORK
5.1 MLLM HALLUCINATION MECHANISM
Hallucination in MLLMs, characterized by contradictions between image input and textual output, has been a prevalent issue (Liu et al., 2024a; Chen et al., 2024g). Current research on the mechanism of hallucination in MLLMs focuses on two key aspects: the interaction between images and text at different layers, and the prior bias of the LLM during decoding. Several studies have investigated the role of image-text interaction at different layers in MILMM. Grad-CAM (Zhang al., 2024a) visu- alizations reveal that image-text interaction exists in the preceding layers (1-11) but not in the deep layers. OPERA (Huang et al., 2024) further proposes that the "Aggregation Pattern" leads to hallu- cination, where visual information from preceding layers is gradually aggregated to anchor tokens, and focusing solely on these tokens during prediction while ignoring visual information leads high probability of hallucination in the generated sequence. However, other studies have revealed that MLLMs exhibit biases towards LLM priors, even in the presence of noisy or absent visual in- formation. VCD (Leng et al., 2023) discovers that MILMs generate high-confidence answers even when the image is noisy or absent, indicating bias towards I.I.M priors Similarly, PAI (Liu et al., 2024b) describes this phenomenon as "Text Inertia" and posits that it stems from existing paradigms that map visual representations onto the text representations as tokens. This leads to an inference process that fails to adequately account for image tokens, resulting in hallucinations.
5.2 HALLUCINATION MITIGATION FOR MILMS
One straightforward approach to mitigate hallucination is to reduce the knowledge gaps and data bias between vision and language during model training. Finetuning- based methods have been explored, focusing on crafting specific datasets (You et al., 2024; Gunjal et al., 2024; Chen et al., 2024f) and alignment training (Sun et al., 2023; Yu et al., 2023; Chen et al., 2023; Li et al., 2023c) to achieve better knowledge alignment between images and text. While these methods have shown promising results, they often require expensive annotated paired data and substantial computational resources.
Hallucination can also be mitigated by post-processing methods, which usually involve using ad- ditional tools or self-reficction strategies to revise the response. For instance, LURE (Zhou et al., 2024) detects hallucinations using maruall features and revises the generated text accord- ingly. Woodpecker (Yin et al., 2023) combines MILM outputs with an expert VQA model to post- edit ballucinations. However, these methods incur additional inference costs and delays, and require task-specific procedures and prompts to be designed (Xu et al., 2024). Training-free decoding meth- ods have been explored to mitigate hallucination. OPERA (Huang et al., 2024) identifies an abnor- mal attention pattern that often accompanies hallucinated descriptions and proposes the mitigation method based on this pattern. VCD (Leng et al., 2023) introduces the notion that visual uncertainty increases hallucination and proposes a contrast decoding method to alleviate the issue. VDD (Zhang et al., 2024b) proposes a "Post-Hoc debias" approach that ensures uniform scores for each answer in the absence of image to mitigate the influence of LLM priors.
6 CONCLUSION
In this paper, we demonstrate that MLLMs exhibit an awareness of hallucinated objects, with earlier layers showing higher confidence, while tokens shaped by prior knowledge diminish the likelihood of true tokens in the final layers. Based this insight, introduce DeCo, dynamic correction de- coding with preceding- layer knowledge to mitigate hallucinations. Extensive experiments demon- strate the efficacy of our approach, which also shows advantages in latency and throughput.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (No. 62206246, No. NSFCU23B2055, No. NSFCU19B2027), the Fundamental Research Funds for the Central Univer- sities (226- 036-2023-001133 00) Yongjiang Talent Introduction Programme A-166G-) CIPSC-SMP- Zhipu Large Model Cross-Disciplinarr Fund, Ningbo Science and Technology Special Projects un- der Grant No. 20232212, Information Technology Center and State Key Lab of CAD&CG, Zhejiang University, NUS-NCS Joint Laboratory (A-0008542-00-00), the Scientific Research Starting Foun- dation of Nanjing University of Aeronautics and Astronautics (No. 101 YAH224996,, the High Per- formance Computing Platform of Nanjing University of Aeronautics and Astronautics, the Ministry of Education, Singapore, under the Academic Research Fund Tier I (FY2023) (Grant A-8001996 00-00), NUS-NCS Joint Laboratory (A (A-0008542-01 -00). We gratefully acknowledge the support of Zhejiang University Education Foundation Qizhen Scholar Foundation.
REPRODUCIBILITY STATEMENT
We have submitted the relevant code in the supplementary materials. The names of the experimental benchmarks, the prompt templates used, and the model's hyperparameter settings can all be found in Section 4. The Appendix C.I and C.2 provides a detailed description of the experimental setup for the mechanism experiments.
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Tian Ye, Zicheng Xu, Yuanzhi Li, and Zeyuan Allen-Zhn Zhu. Physics of language models: Part 2.1, grade-school math and the hidden reasoning process. CoRR, abs/2407. 20311, 2024. doi: 10. 48550WARXIV.2077.00 20311. URL https:/ //t////ooooo /ooi. org/100...... 0tt//1.485550 48550/ w...v. 2407. 20311.
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Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision- lasion-lagguag understanding with advanced large language models. In The Tweifth Interna- tional Conference Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. URL https:/ /ttp////wpeeeeeee/e/e/ee// net wwt.:roumtt//o/ee/mettmmam denenndd=1tttt dtt.rmmmd111772t88-f...
A LIMITATIONS
Lack generalized research. Due to the GPU cost consideration, we conduct experiments solely on limited MLLMs, without exploring additional MLLMs those with larger parameter sizes.
No free lunch. The results shown in Table 4 indicate that our method has little negative impact on the level of detailedness metric. In future work, we aim to integrate DeCo with other strategies and explore approaches that can effectively balance truthfulness and diversity.
B COMPARATIVE ANALYSIS, SUMMARY, AND FUTURE DIRECTIONS
Here, we compare work with previous works, summarize and speculate the underlying causes of hallucinations in LLMs and MILMs, and provide insights into future directions.
Comparison of previous mechanism findings. Existing studies suggest that MLLMs may focus more on visual tokens the early layers while paying greater attention to textual tokens in the later layers (Zhang et al., 2024a; Chen et al., 2024c). The aggregation pattern is typically positively correlated with hallucinations and tends to emerge at deeper layers (Huang et al., 2024). These conclusions align with our findings, suggesting that MLLMs exhibit a better ability perceive visual information the preceding- layers compared to the final layers. the detection of hallucinations in LLMs, some studies employing probing techniques have found that the intermediate layers exhibit the best detection performance (Chen et al., 2024a; Orgad et al., 2024; Chen et al., 2024e; Lu et al., 2024b), finding similar to our Finding 1. This suggests that the hallucination mechanisms in LLMs and MLLMs may share underlying similarities.
Comparison of previous hallucination mitigation methods. Our work shares similar assump- tion with OPERA (Huang et al., 2024) and VCD (Leng et al., 2023), positing that the knowledge priors inherent in MLLMs may suppress the model's ability to comprehend visual information. However, our approach is comparatively simpler than that of OPERA (Huang et al., 2024) and VCD (Leng et al., 2023). Additionally, our work differs from the assumption in unimodal LLMs, where the semantic information present in the shallow layers interferes with factual recall in the final layer (Chuang al., 2024; Chen et al., 2024d). However, our method is actually parallel previous approaches and can be combined to achieve better results.
Summary. Combining current research, we speculate that this phenomenon observed in both LLMs and MLLMs may be due to characteristics of the Transformer architecture, specifically the anchor token effect in the attention mechanism (Huang et al., 2024; Wang et al., 2023b), which leads to information loss when processing long sequences. For instance, in MLLMs, a single token may be insufficient to summarize information from extended sequences of visual tokens. Another work suggest that the knowledge overshadowing of multiple conditions within the query leads to hallucinations in LLM (Zhang et al., 2024c). In multimodal settings, image information represents a distinct condition. When the textual modality overshadows the condition related to the image, it can result in hallucinations in visual perception. Essentially, this reflects loss of information flow within the attention mechanism. Overall, from an architectural perspective, hallucinations in both LLMs and MLLMs arise due to the imperfect of handling such interactions within the attention patterns of the Transformer.
Future directions. Overall, the present works and ours work reveals notable similarities in the internal patterns of hallucination between LLMs and MLLMs. future research, we will adopt a unified perspective investigate the underlying causes of hallucinations in both L.MMs and MI Ms..Ms.
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C DETAILED EXPERIMENTAL SETUP
C.I DETAILED SETTINGS FOR FINDINGS 1
In the probing experiment, we utilize the pipeline proposed in the POPE (Li et al., 2023b) con- struct 1,200 balanced positive and negative sample pairs from the MSCOCO dataset as training data for the probe classifier, where each sample consists of an object accompanied by a label indicating its existence non-existence, (Note: There is no overlap between the training data and the evalu- ation data for object hallucination proposed by the POPE). We select the AMBER dataset (Wang et al., 2023a), which has a different distribution from the MSCOCO dataset, test whether our conclusions can generalize. The AMBER dataset contains 1,004 carefully annotated images, cach labeled with existent objects well as non-cxistent objects. We use the prompt "Describe the image." to generate raw responses from LLaVA-1.5 on the images and then extract all object category tokens and label them with whether they exist. Given that the training set contains only 80 object categories, we denote the object tokens in test data belonging to these 80 categories as in-distribution (in-dist), while the remaining tokens are categorized as out-of-distribttion (OOD).
Previous work (Karamcheti et al., 2024) has demonstrated that increasing the resolution of the vi- sion encoder enhances the visual comprehension capabilities of MLLMs. In our study, we compare LLaVA trained with resolution of 224px against the original LLaVA with a resolution of 336px in probing experiments. Notably, the language model's weights differ between the two MLLMs, al- though both initial models are based on Vicuna- (.aua-1.5--0 1.5-7b. Our results, as illustrated in the Figure (b), further affirm the scaling law associated with visual resolution, while also providing indirect valida- tion of the reliability of the probing experiments.
C.2 DETAILED SETTINGS FOR FINDINGS 2
In the early exit experiment, we randomly select 500 images from MSOCO and use random prompts (shown in Table 6) to elicit raw responses from LLaVA-1.5-7b. We then extract all non-existent objects along with their corresponding pre- ceding text. Specifically, for the sentence "Additionally, there is car.". " we extract the hallucinated object token "car" and the preceding text "Additis sadditionally ly, there is a". We re-input the preceding text into the
Table 6: Randomly prompts.
Prompts
Describe the image. Please describe this image detail.
Generate a caption for this image.
MLLM and observe the changes in its internal state when pre-
dicting the next token. We denote that a total of K preceding texts are selected, with the j-th preced- ing text denoted as s².
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D EVALUATION RESULTS IN AMBER
The AMBER image caption dataset consists of 1,004 images, each accompanied by meticulously annotated labels. These annotations include all objects present in the images, well as some po- tential hallucinated objects. AMBER employs four evaluation metrics: CHAIR (the proportion of generated hallucinated objects among all objects), Cover (the coverage of generated objects against all ground truth objects). Hal (the proportion of hallucinations among all generated captions), and Cog (the overlap ratio with potential hallucinated objects). Lower values of CHAIR, Hal, and Cog indicate higher truthfulness for the ML..Ms, while higher Cover value signifies better diversity. We compare Deco with the baselines on the L.L.VA-1.5577 The results are as shown Table The results reveal that Deco demonstrates significant advantage in truthfulness, although its diversity somewhat lacking, yet remains within an acceptable range.
Table 7: Results of using DeCo on the AMBER image caption dataset with LLaVA-1.5-1
LLa
Decoding
Method
CHAIR, 4
Cover t
Hal 4
Cog +
Vanilla
8.2
48.9
34.3
4.0
Greedy
DoLa
8.0
50.8
37.5
4.3
DeCo (Ours)
6.6 J1.66
47.5 L1.4
28.1 J6.2
2.8 J1.2
Vanilla
7.1
50.7
32.4
3.8
Beam Search
OPERA
6.4
49.0
27.5
DeCo (Ours)
6.3 J0.8
46.8 L3.9
25.1 47.3
2.4 J1.4
Vanilla
10.2
50.2
43.3
4.5
Nucleus
VCD
9.0
51.7
40.2
4.4
DeCo (Ours)
41.9
48.0 2.2
37.5 15.8
3.4 11.1
E ABLATION RESULTS OF DYNAMIC SOFT MODULATION
To quantify the effect of soft modulation, we remove the to describe the images. The images and prompts used in the ablation experiment are consistent with the setup in Table and the ablation results are presented in the Table 8. Additionally, we provide illustrative cases that demonstrate how soft modulation helps prevent abrupt changes in logits, as shown in the Figure 13.
term and greedy decoding
Table 8: Ablation study of dynamic soft modulation.
LLaVA-I.5
Qwen- WL
Method
CHAIRs
CHAIR; +
CHAIRs 4
CHAIR; +
DeCo
37.8
11.1
42.2
10.7
DeCo (wo/max-probs)
41.2 13.4
11.6
10.5
45.8 13.6
12.3 116
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F RESULTS OF MMVET
We evaluate the performance of (Lava-1155775 and Qwen-VIL-7B VL--BB on a comprehensive benchmark MMVet and their default settings based nucleus sampling. The results are as shown in Table 9.
Table 9: Results of using DeCo on MM with LLa LLa and Qwen-VL-7b. VL-7b.
Model
Method
Rec 7
OCR
Know
Gen
Spat
Math
Total 1
LLaVA-1.5
Vanilla
28.8
14.1
15.5
16.4
15.6
3.5
23.6
DeCo (Ours)
32.1 13.3
21.5 17.4
18.6 13.1
20.7 14.3
23.7 18.1
11,2 17.7
27.9 14.3
Vanilla
51.8
35,3
41.0
35.6
38.1
19.2
45.7
Qwen-
DeCo (Ours)
50.5 11.3
38.2 12.9
38.8 12.2
33.8 L1.8
41.7 13.6
26.5 17.3
46.3 10.6
G CASE ANALYSIS ACROSS DIVERSE MLLMS
Describe the image.
Greedy Search
there is car parked on the street, and person can be seen standing near the corner. The scene captures the essence of bustling urban environment with clear signage to help people navigate their way around the city.
User
RICHMOND LIBRARY WEST MARIET
The image showcases street corner in city, featuring street signs mounted on street light post. The signs provide directions various locations, including the local library and nearby market. The sky above the street corner is blue, indicating clear day.
Ours
there is parked near the corner possibly parked the parking meter indicated by one The scene captures the essence of urban life and navigation, with clear directions for pedestrians and drivers alike.
MLLMs
street signs.
In the background, there is car ???
Figure 8: The case of mitigating snowballing hallucination with DeCo.
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Instruction:
Image detail.
InstructBlip (Greedy)-
The image features large red chair with the shape of an animal, specifically in this case being represented by two horses. One horse standing on top and onother one oppeors to be sitting atop it from behind its bock legs towards front side viewing angle. There several people visible throughout different parts around individuals
these animals: three
walking
close together:
further away who seems more distont still within reachable distance for interaction needed, Additionally, some cars parked nearby, add movement into scene as they come closer while driving past each other during their daily activitiss outside.
(Ours):
The image features a large red chair with horse statue placed on top of it. horse statue positiened the chair's seat, making chair.
if the horse actually sitting placed grassy area, likely outside, trees visible in the background. The scene is set sculpture, showcasing unique and creative way displaying horse
are
Input Image
Instruction: Please describe this image detail.
InstructBlip (Beam search):
winding road by tall, rocky cliffs both sides, creating a dramatic and scenic setting. The motorcyclists are wearing helmets and appear to be enjoying the ride, taking in the breathtaking views around them. There are two matorcycles visible in the with closer the right parked along side the road, providing additional transportation options for those exploring the area.
The image depicts group of three motercycliats riding through a
of the image and the other slightly off motorcycles.
are
InstructBlip (Ours):
The image depicts group of matorcycles traveling down roadway, passing through tunnel. The motorcycles are ridden by three people who are enjoying the scenic route through the tunnel, The motorcycles are positioned various positions along roadway, with motorcycle being image,
motorcycle slightly behind it, another motorcycle further away from center of the image. third matorcycle is positioned further away from the others, but still visible in the background. The readway itself surrounded by trees and rocks. adding to the scenic nature of the
Input Image
Instruction: Please describe this image in detail.
InstructBlip (Nacleus):
The image captures skateboarder skilifully riding rail in blue skate pork, likely practicing tricks. The skateboarder weoring helmet, providing safety for activity. parked on either side multiple cars present scattered around the scene, likely providing seating for the skateboarder and other park visitors.
background, including two skate park. Additionally, there are two benches
InstructBlip (Ours):
image depicts skateboorder skilifully skating a skateboarding park. The skateboarder is wearing helmet and appears to be engoging trick, performing rail grind the rail. The skateboording park well-equipped
railing
skateboarding obstacles, including skatebourding romp, skateboarding rail, and skateboarding ledge. These obstacles are designed challenge skateboarders and provide them various ways.
opportunity
Input Image
practice skills showcase
Figure 9: DeCo's performance in reducing hallucinations of InstructBlip-P7 on three basic decoing methods.
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Instruction: Please describe this image in detail.
LLaVA-1.5 (Greedy):
The image features woman sitting matorcycle, which parked front of white building.
oppeors posing the motorcycle is positioned in the foreground, with the woman sitting confortably on the seat,
In the background, there parked further away from metorcycle. The coptures the woman's enthusicsm for her motorcycle and her outdoor adventures.
LLaVA-1.5 (Ours):
The image features woman sitting motorcycle parked outside building. She is motorcycle
positioned
motorcycle, occupylng a majority itself accupies a large portion of the scene, extending from left to right.
woman appears to dressed in motorcycle gear, possibly preparing already having completed one. The motorcycle's positioning suggests that it might be parked outside garage other similar location where motorcycle enthusiasts gather.
Input Image
Instruction: Please describe this image detail.
LLaVA-1.5 (Beam search):
delicious- looking pizza sitting table. The pizza is topped with tomatoes and cheese, making it an appetizing sight. Next to the pizza, there bottle of beer, which complements the perfectly.
image
dining
addition to the pizza and beer, there knife placed on table, likely for cutting and serving the pizza slices. The dining table occupies most space image, emphasizing
pizzo
LLaVA-1.5 (Ours):
The image features delicious looking pizzo sitting inside box, ready to pizza topped delicious and freshly prepared, Next to the pizza box, there is a bottle of
making
beer, adding the overall enjoyment of the meal, The pizza
be
ready for
enjoy
Input Image
InsTruction: Please descriße This defail.
LLaVA-1.5 115 (Nucleus):
The features group four giraffes lush field, surrounded
pond. giraffes standing other, with one on the left side, two in the middle, and one on the right side of the scene. They appear to be enjoying their time in the open field, possibly z0o wildlife sanctuary.
near left giraffes, the other closer to center. The birds likely part of the wildlife in the area. The averall atmosphere of the image is serene and peaceful, showcasing the beauty of the natural environment.
birds
the
LLeVA-1.3 (Ours):
The image depicts group of four giraffes in zoo enclesure, enjoying their tine together the grassy They spread out throughout the enclosure, onother right side, one close to middle, and another giraffe further back background.
Input Image
The enclesure features pond middle, where the giraffes drink off during hot days. alse enclosure, providing
natural
Figure 10: DeCo's performance in reducing hallucinations of LLaVA-1.5-7B on three basic decoing methods.
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Instruction: Describe this image in detall.
Qwen-VL (Greedy)::
The inage features young woman stunding a doorway of bothroom, She is holding toothbrush her hand and appears to be smiling as she pases for the comero. bethroom window, allows
light to enter room.
In the background. there clock the wall, located above door handle. The on leading into bathroom. womon standing near the door, creating frome with her body that coptures her and the doorway.
Qwen-VL VL (Ours):
image features young woman standing deorway holding toothbrush in her hand and smiling for the comera. The bathroom oppears be old ald-fashioned style, with 1999 date the bottom right corner image. women's heir styled bun, she gray sweater and jeons.
7. 199
Input Image
Instruction: Describe this image in detail.
(Beam search):
The image coptures man skiing down a snow-covered: slope at high speed. He wearing ski gear and skiing equipment, including skis on his feet. The man is fully immersed in the skiing experience, leaning forword slightly he novigates the snowy hill.
There are two ski poles visible the scene, one on the left side and the other on the right side of the mon. Additionally, backpock con be spotted in the background, likely carrying the skier's belongings.
Qwen-VL VL (Ours):
The features wearing brown jacket and hat skiing down with two poles for balance and control snowy He The octively making way through during snow, enjoying the winter sport. The scone is set snow--oovered landscape, providing fun and challenging enviroment for skiing.
Input Image
image detail.
Qwen-VL (Nucleus):
The image features two zebros standing side by side a zoo or a wildlife park. They are both eating hay from a trough placed in their enclesure. The zebras have distinctive black and white stripes that are easily recognized.
area, either
background, there several rocks the scene natural feel. person can be seen near the right side of the image, possibly observing maintaining the enclosure.
area, giving
Qwen-VL. (Ours):
The image features two zebras standing side by from a trough. They both have a distinctive black and white striped pattern their coots. The first zebra is located more towards the left side of the image, while the second zebra is slightly further to the right.
field, eating hay
reserve a zoo, suggested by the presence a feeding station. The overall setting suggests colm and peaceful environment, allowing these two zebras to graze together in harmony.
appears
part
Input Image
Figure 11: DeCo's performance in reducing hallucinations of Owen-VL--7B three basic decoing methods.
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Instruction: Please describe this image detail.
MiniGPT4 (Greedy):
This Image shows
holding white object his right hand, while other man standing behind him with his orms cressed front of his chest. The room decorated yellow various objects, including lamp, vase, and book. The walls are adorned with yellow and white wallpaper. The everall atmesphere of the image
standing living
furniture
relaxed.
MiniGPT4 (Ours):
image depicts two men standing a room with yellow curteins. One mon holding white object in his right hand while the other mon standing behind him with his arms cressed in front of him. The furmiture the includes blue couch and chair. The sunlight coming through window.
oppeors
Input Image
image
MiniGPT4 (Beom search): image
large clock tower made of metal and has two large clock faces on it. The clock tower surrounded by tall buildings on either side of it. There is black car parked on the sidewalk next to the clock tower. The car is facing the clock tower and has its lights There people walking the sidewalk front of the clock tower. looking
middle city
clock
walking past The buildings on either side the clock tower have large windows and balconies. The street lined with trees and streetlights.
MiniGPT4 (Ours):
This image depicts a large clock tower in the middle of city street. The clock tower made of metal and has two clock faces it. The clock tower is surrounded buildings and passing by the street below It. There parked
tower.
Input Image
Instruction: Please describe this detail.
MiniGPT4 (Nucleus):
The shows white plate with of meat, bunch of broccoli, and knife fork of water the side. plate brown tablecloth background brown wall.
plate, glass
MinigPT4 (Ours):
image plate consisting broccoli, potatoes, meat. The plate white with black rim and appears on table or countertop. The food is arranged in rows with broccoll on one side the plate and potatoes the other side. piece of meat in the center of the plate.
Input Image
Figure 12: DeCo's performance in reducing hallucinations of MiniGPT4-7B on three basic decoing methods.
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Instruction: Please describe this image in detail.
Ours:
The depicts bedreom where bed occupies large portion of the space. bed itself is resembling circular shope with center. bedroom from other parts of the room.
divider
separates bed
There two pillows the bed. each side, adding confort to the bedding arrangement. The bedroom also features wall with few windows, allowing
light illuminate
Ours (wo/max (ve/max_probb):
The depicts bedroom where bed occupies most of the space. bed unique
circulan frame suspended shope style: resembles It appears metal and has bedding arrangement consisting of pillows and blankets placed on top of it.
of
There pillows visible the bed, locattd the middle of and
Input Image
the part Additionally. two blankets can seen on the bed frame, one occupying the middle part of it and another one slightly higher than the first one.
Instruction: Please describe this image detail.
Ours:
The depicts giraffes standing together inside enclosure.
They both tall and majestic animals, eccupying majority the space image while the other occupies smaller portion. Both giaffees are feeding from feeding basket hung high above them, likely providing them with food to eat.
The enclosure features concrete walls and solid structure for animals to stand on. scene captures the beauty of these two majestic animals interacting with each other and feeding together.
floor, creating
Ours (wa/max__pt probb):
image depicts giraffes stonding together inside zoo enclosure, They ane both tall and majestic animals, one occupying the left side of the frome while other occupies the right side. Both giraffes feeding on hay placed high above them, likely hung from a feeding pole or similar device.
The zoo enclosure features concrete walls and a concrete floor, creating solid structure for the onimals to stand within, Additionally. thene are two clocks visible in the background, one lecated above the other, likely staff track during feeding other activities enclosure.
Input Image
Instruction: Please describe this image detail.
Ours:
The depicts young standing next two horses grassy they
field. The boy holding the together. The horses are positioned close to each other, one accupying the left side of the field and the other eccupying the right side.
reins, guiding
shirt, and he appears to be The wearing jeans and The captures moment enjoying his time bonding between boy and horses, showcosing connection between humons and animals.
Ours (wo/max, probs):
image depicts young boy standing next two horses, one and one black, both equipped with hamessss. The boy seems to be handling both horses simultaneously, possibly preparing them for a ride or simply admiring them.
horses, one accupying side of frome while the other accupies the right side of the frome, Both horses are equipped with harnesses, one hamessed nearer to the boy' 's left side while the other hamnessed nearer to his right side.
positioned
Input Image
Figure 13: Analysis of cases of soft modulation. Soft modulation avoids the hallucination phenom- ena and erroneous semantics caused by abrupt changes in logits.
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Table 10: The prompt used for GPT-4o evaluation adopted from Leng et al. (2023); Huang et al. (2024); Liu et al. (2024b)
GPT-40 Prompt
You are required score the performance of Al assistants in describing given image. You should pay extra attention to the hallucination, which refers to the part of descriptions that are in- consistent with the image content, such as claiming the existence something not present in the image describing incorrectly in terms of the counts, positions, colors of objects in the image. Please rate the responses of the assistants on a scale of 10, where a higher score indicates better performance, according to the following criteria:
1: Accuracy: whether the response is accurate with respect to the image content. Responses with fewer hallucinations should be given higher scores.
2: Detailedness: whether the response is rich in necessary details. Note that hallucinated descriptions should not count as necessary details.
Please output the scores for each criterion, containing only two values indicating the scores for Assistant and respectively. The two scores are separated by space. Following the scores, please provide explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.
[Assistant I]
[Response of Assistant }} [End of Assistant 1]
[Assistant 2]
[Response of Assistant 2}
[End of Assistant 2]
Output format:
Accuracy: <Scores of the two answers>
Reason:
Detailedness: <Scores of the two answers>
Reason:
Table 11: The prompt used for GPT-4o to evaluate coherence.
GPT-4o Prompt
You are required to score the coherence of two Al assistants in describing given image. Please rate the responses of the assistants on a scale of 1 to 10, where a higher score indicates better coherence.
[Assistant I]
[Response of Assistant 1} [End of Assistant 1]
[Assistant 2]
[Response Assistant 2}
[End of Assistant 2]
Output format: Coherence: <Scores of the two answers > Reason:
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Reviews:
[{"review_id": "REb8ztCB1E", "reviewer": "Reviewer_vNi9", "summary": "The paper introduces DeCo (Dynamic Correction Decoding), a decoding technique to mitigate hallucinations in Multimodal Large Language Models (MLLMs). The authors identify that MLLMs are capable of recognizing objects in earlier layers, but this recognition is suppressed in deeper layers by strong language model priors, which leads to hallucinations. DeCo dynamically integrates the output from preceding layers, which contain higher probabilities for ground-truth tokens, into the final layer logits to enhance visual grounding and suppress hallucinations. Experimental results on datasets such as CHAIR, POPE, MME and GPT-4o assisted evaluation demonstrate DeCo’s significant improvements over baselines in hallucination suppression across multiple MLLMs, with manageable latency increases, highlighting its practical applicability.", "questions": "- Given that DeCo's effectiveness depends on selecting an optimal layer range (e.g., 20-28), does the layer range need tuning for different MLLMs?\n- Could you provide more details on the selection process for the 500 images used in experiments? Additionally, which split(s) were used to determine and evaluate the hyperparameters, and were any specific criteria applied for these selections?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The authors demonstrate through probing experiments that MLLMs can recognize objects in earlier layers but tend to “forget” this information due to language model priors in deeper layers, leading to hallucinations. This insight offers a novel layer-wise perspective on the hallucination mechanism in MLLMs.\n- The figures illustrating token probabilities across transformer layers effectively highlight the trends for hallucinated versus non-hallucinated tokens, making the analysis accessible and informative.\n- Compared to existing methods like VCD and OPERA, DeCo achieves similar or better hallucination suppression with lower latency overhead, enhancing its practicality for real-world applications.\n- Evaluation across diverse benchmarks (CHAIR, POPE, and MME) and several models (InstructBLIP, MiniGPT-4, LLaVA-1.5, and Qwen-VL) provides a well-rounded assessment of DeCo’s effectiveness.", "weaknesses": "- In Figure 9, the response includes awkward repetition, with \"The horse statue is positioned on top of the chair\" stated multiple times. This raises questions about the effectiveness of the chosen α\\alphaα value in avoiding repetitive language, as the authors indicated that high \\alpha values could increase repetition.\n- In Figure 10, DeCo reduces a significant hallucination (misidentifying a lift as a \"chair\"), but the output still contains a hallucination about \"several other people visible in the background.\" This discrepancy between benchmark performance and qualitative examples suggests that DeCo’s effectiveness might not fully translate into consistently accurate real-world responses.\n- For each time step tt, language tokens that are not related to the visual input but are essential for sentence generation may be influenced as they pass through the proposed method. There appears to be a lack of investigation into the nature of this influence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3VGFPTf3Dk", "reviewer": "Reviewer_tgHP", "summary": "This paper demonstrates that while MLLMs may produce incorrect target outputs in the final layer, they effectively recognize visual objects in the preceding layers. The authors propose a dynamic correction decoding method for MLLMs (DeCo), which adaptively selects relevant preceding layers and proportionally integrates their knowledge into the final layer to adjust the output logits. The proposed method outperforms existing approaches on public datasets.", "questions": "My primary concern lies in the potentially low quality of text generated from the preceding layers. I will be happy to raise my score if my current questions and concerns can be addressed.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation seems interesting.\n2. The paper is well written and easy to follow. The diagrams are essential to understanding this paper.\n3. This paper achieves good results on existing datasets.\n4. The main technical pipeline is clear.", "weaknesses": "1. Although the experiments indicate improved performance in preceding layers, I am concerned about the coherence and richness of the text generated at these stages. Could you provide further evaluation metrics for text quality, such as BLEU or other relevant scores?\n2. In Figure 1(b), the interval [10, 20] appears optimal, yet in Figure 7(b), [17, 28] shows better performance. Could you clarify this discrepancy?\n3. Could you provide more evidence to demonstrate how dynamic soft modulation prevents abrupt changes in logits? Additional ablation studies might further substantiate this claim.\n4. Could you share detailed MME results to highlight the method's performance across different subtasks?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jD0HK46qz6", "reviewer": "Reviewer_jCUa", "summary": "This paper makes the observation that MLLMs' internal prior suppresses the visual information, thus leading to hallucination. Besides, they empirically observe that intermediate layers may have less such suppression. Motivated by this observation, this work proposes to combine intermediate logits with final layer projection, and demonstrate improvement in reducing hallucination via empirical study.", "questions": "- It's unclear to me how is the affine layer $\\phi$, in L104, initialized and trained, if at all? If it needs training, then it seems each layer needs such a layer. If it doesn't, then how do we make sure that resentation across layers can share the same mapping to present token probability?\n- POPE evaluates answers in Yes/No. How could decoding strategy have impact on the performance for this benchmark?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- This work makes an interesting observation of how visual information exists in intermediate layers, and then overridden by knowledge prior closer to the output\n- The proposed mitigation method is lightweight and efficient.\n- The experimental results are in general better than baselines.", "weaknesses": "Although the presentation has a focus about image-conditioned generative language model, the methodology for finding 1 and 2, as well as the proposed layer selection and probability correction, are modality agnostic. The findings are mostly empirical, and it's unclear whether this is a general phenomenum for other models in the same size, nor for models in other sizes. \n\nThere has been quite a few literature in studying LLM's internal presentation and hallucination, only selectively listing a few as [1-5] . What the multi-modal setting brings is the strong conditional dependency, while for text-only use cases there might or might not be informative conditions. An analytical comparison on how an MLLM focuses or ignores input conditions can be more informative and persuasive in supporting the methodology. \n\nIn L191-L201 the paper compares the token output with and without the image condition. However this has been studied thouroughly in [6], which also proposes hallucination detection and mitigation method.\n\nThe method design also seems ad-hoc, there are thresholds in Eq2 and Eq3, layer interval a, b in Eq4 and the weight $\\alpha$ in Eq7. Together they contribute to amplifying the concern in the generalizability of the proposed method.\n\nI suggest to connect the empirical evidences in this paper to 1/ evidences from other papers with the same spirit, and 2/ the unique property and behavior of conditional multi-modal modeling. \n\n**Reference**\n\n[1] Liu, Junteng, et al. \"On the Universal Truthfulness Hyperplane Inside LLMs.\" arXiv preprint arXiv:2407.08582 (2024).\n\n[2] Li, Kenneth, et al. \"Inference-time intervention: Eliciting truthful answers from a language model.\" Advances in Neural Information Processing Systems 36 (2024).\n\n[3] Zhang, Tianhang, et al. \"Enhancing uncertainty-based hallucination detection with stronger focus.\" arXiv preprint arXiv:2311.13230 (2023).\n\n[4] Azaria, Amos, and Tom Mitchell. \"The internal state of an LLM knows when it's lying.\" arXiv preprint arXiv:2304.13734 (2023).\n\n[5] Duan, Hanyu, Yi Yang, and Kar Yan Tam. \"Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States.\" arXiv preprint arXiv:2402.09733 (2024).\n\n[6] Favero, Alessandro, et al. \"Multi-modal hallucination control by visual information grounding.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gdi2i5S8e9", "reviewer": "Reviewer_QBQj", "summary": "The paper investigates why MLLMs generate hallucinations, particularly in image captioning tasks, and introduces DeCo, an innovative method that leverages knowledge from earlier layers to reduce hallucinations during inference. However, I still have some concerns about this article, specifically in regard to the weaknesses.\nIf these concerns are addressed, I will consider raising my score.", "questions": "- Could you clarify whether Finding 1 in Section 2.1 in the section is related to the methodology of the paper? It seems that embedding-level knowledge wasn't used to assist the model.\n- Could you follow LLAVA’s setting and conduct more extensive evaluations on comprehensive benchmarks like MMbench and MMVet, given their importance for assessing the model's overall performance?\n-  Could the authors clarify the specific settings followed in the experiments presented in Table 3?  How do these settings differ from those used in LLaVA?\n- Is this decoding method useful in more advanced VLLMs, such as Qwen-VL, VILA, etc.?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper provides a detailed examination of why MLLMs generate non-existent objects, offering valuable insights into the hallucination issue in image captioning tasks.\n\n-  The introduction of DeCo is innovative, using preceding-layer knowledge to reduce hallucinations during inference, effectively improving output accuracy.\n\n- The method of probing across transformer layers reveals how hallucinations emerge in later layers, helping to understand MLLM behavior better.", "weaknesses": "- I am confused by the experimental results about POPE in Table 3, as they do not seem to fully align with the result from LLAVA 1.5. \n- The authors did not perform more extensive evaluations on comprehensive benchmark such as MMbench and MMVet, which are crucial for assessing the model's overall performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)