MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

## Abstract

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 1 1 1 Code is available in https://github.com/zjunlp/DeCo . .

## 1 Introduction

Recently, the rapid development of Multimodal Large Language Models (MLLMs) has demonstrated a potential pathway towards achieving Artificial General Intelligence (AGI)  (Wang et al., ; Yao et al., ; Lu et al., ; Team, ; OpenAI, ; Liu et al., ; Chern et al., ).
However, in practice, the development of MLLMs is hindered by the phenomenon of hallucination, which typically results in the model generating statements about non-existent images while neglecting to mention certain visible objects, effectively causing it to fool itself (Bai et al., ; Liu et al., ; Li et al., ; Liu et al., ; Rawte et al., ).
This issue poses significant risks in high-stakes fields such as medical imaging (Chen et al., ; Hu et al., ; Wang et al., ), autonomous driving (Cui et al., ; Wang et al., ), and human-computer interaction systems (Brie et al., ), where such errors could result in irreparable consequences.

The reasons behind hallucinations in MLLMs are complex.
Unlike analyses focused on unimodal LLMs (Chuang et al., ; Chen et al., ; Orgad et al., ; Chen et al., ; Lu et al., ), many current works assume that MLLM may indeed ‘see’ visual information.
However, due to factors such as excessive model depth (Chen et al., ; Zhang et al., ), aggregation patterns (Huang et al., ), or priors knowledge inherent in the MLLMs (Leng et al., ; Zhang et al., ), these models ultimately still experience hallucinations.
Concretely, our understanding of the underlying mechanisms of hallucinations in MLLMs 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.

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(^2^22This approach is applicable to other types of hallucinations as well.) and conduct experiments with MLLMs, demonstrating that they know to some extent whether an object exists (as shown in Figure 1 and Section 2.1).
We further observe that the confidence of generated tokens is influenced by the knowledge priors of MLLMs (Section 2.2), leading to a reduction in the probability of ground truth tokens in the deeper layers.

Dynamic correction decoding with preceding-layer knowledge.
Based on those findings, we propose Dynamic Correction Decoding with preCeding-Layer Knowledge (DeCo) to mitigate hallucinations for MLLMs.
Our core hypothesis is that preceding layers exhibit higher confidence for ground truth tokens, and the logits for these tokens should rank prominently at the last layer’s outputs.
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 a 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 as greedy search, nucleus sampling as well as beam search, and can seamlessly incorporate into any MLLMs for hallucination mitigation.

Contributions.
Our primary contribution lies in 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 a reduction in the probability of ground truth tokens in the deeper layers.
We further propose DeCo, a dynamic correction decoding method guided by preceding-layer knowledge.
DeCo is 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 baselines on visual question answering datasets including POPE, and MME.
Additionally, we analyze the latency and throughput, showing that DeCo introduces an approximate 1.2x increase in latency compared to the basic decoding process, much faster than previous baselines such as VCD and OPERA.

Figure: (a) Object probing results.
Refer to caption: x1.png

## 2 Why do MLLMs generate non-exist objects?

In this section, we conduct a series of empirical analysis to investigate the internal mechanisms of MLLM and elucidate the underlying reasons for its generation of non-existent objects. To strike a balance between the realism and complexity of the experiments, we primarily focus on the generation of objects in image description scenarios (image caption tasks).

#### Preliminaries of MLLM generation.

MLLMs 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 $\mathbf{X}^{V}=\{x_{v_{1}},x_{v_{2}},\ldots,x_{v_{P}}\}$ and textual tokens as $\mathbf{X}^{C}=\{x_{c_{1}},x_{c_{2}},\ldots,x_{c_{Q}}\}$.
Here $P$ and $Q$ are the lengths of the visual tokens and textual tokens respectively.
Finally, the input is $\mathbf{X}=\text{concat}\{\mathbf{X}^{V},\mathbf{X}^{C}\}$.
Then $\mathbf{X}$ would be passed into MLLM with $\mathbf{N}$ stacked transformer layer.
The intermediate variable generated by the i-th layer is called hidden states, denoted as $\mathbf{h}^{i}=\{h_{0}^{i},h_{1}^{i},\ldots,h_{T-1}^{i}\}$, where $T=P+Q$.
During the generation phase, we use the hidden state at the last position in the final layer, which is mapped to the vocabulary dimension through an affine layer $\phi(\cdot)$, to predict the probability of the next token.
Formally, we have:

$$ $\displaystyle p(x_{T}|x_{<T})=\text{softmax}(\phi(h_{T-1}^{N}))_{x_{T}},x_{T} \in\mathcal{V}$ (1) $$

where we use $x_{<T}$ to simplify the sequence $\{x_{i}\}_{i=0}^{T-1}$ and $\mathcal{V}$ refers to the whole vocabulary set.

Figure: 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 a higher probability. In the final layers, hallucinated tokens demonstrate increased probabilities, while the probability of non-hallucinated tokens drops sharply.
Refer to caption: x3.png

### 2.1 Finding 1: MLLM knows to some extent whether an object exists

Inspired by (Ye et al., ), we explore how MLLMs comprehend objects in the image captioning task.
For simplicity, we abstract this process into a function called isexist(obj), which determines whether an object is present in an image.
To examine the application of this function within the MLLM’s image captioning workflow, we conduct probing experiments at the conclusion of object descriptions in each layer of the MLLM’s language model component, which consists of 32 transformer layers in a 7-billion-parameter model (Detailed setup in Appendix A.1).

We employ the prompt template, “USER: <image>Describe the image.
ASSISTANT: The image contains obj.”
Both the training and testing datasets are 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 in a total of 32 classifiers. (For details on the subset division, OOD and in-distribution splits, and prompt templates, please refer to Appendix A.1.) The model is evaluated using the test set, as shown in Figure 1(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-existent objects, the MLLM 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, as illustrated in Figure 1(b), which aligns with previous findings (Zhang et al., ; Leng et al., ).
Furthermore, we show that increasing the resolution of the visual encoder (from 224px to 336px) enhances 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 A.1)
These findings can deepen our understanding of the object comprehension processes in MLLMs and highlight potential avenues for improvement.

### 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 MLLM reduces the probabilities of ground truth tokens in deeper layers. Figure 2 illustrates this hypothesis with running examples. We analyze the Top-4 tokens ranked by probability in the final layer’s output. Non-hallucinated tokens 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 probabilities 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 tokens’ probabilities in the final layer.

Figure: Figure 3: Distribution of activated ground-truth tokens across layers.
Refer to caption: x4.png

To further investigate this phenomenon, we conduct an early exit experiment  (Teerapittayanon et al., ; Elbayad et al., ; Schuster et al., ) 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 behavior (see Appendix A.2 for detailed experimental setup).
The output of the $i$-th layer is denoted as $h^{i}$, and the probability distribution of the next token is represented as $p(\cdot|x_{<s})^{i}=\text{softmax}(\phi(h_{s-1}^{i}))$.
To reduce the observation tokens and simulate the real sampling process, we truncate the vocabulary, similar to Top-$p$ sampling, and obtain the candidate tokens, denoted as $\mathcal{V}_{candidate}$ with a default threshold of 0.9.
We then label the tokens in $\mathcal{V}_{candidate}$. Specifically, we filter out data where $\mathcal{V}_{candidate}$ contains at least one ground truth token and observe whether an activated ground truth token exists among the candidate tokens, formally expressed as:

$$ $\displaystyle\exists\ x_{a}\in\mathcal{V}_{candidate}\land i\in(0,N],\ p(x_{a} |x_{<s})^{i}-p(x_{h}|x_{<s})^{i}\geq\text{threshold},$ (2) $$

where $x_{a}$ is the activated ground truth token, $x_{h}$ is the token with the highest probability of being a hallucinated token in the probability distribution of the final layer and $\text{threshold}\in(0,1)$. Based on the experimental setup described above, we conducted the following investigation:

#### What suppresses the expression of visual facts?

We analyze the occurrence of $x_{a}$ at each decoding 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. However, the activated ground truth tokens are suppressed in the final output layer. This suppression may stem from the guidance of the input image or the inherent knowledge bias of the MLLM. To investigate this, we generate candidate tokens $\mathcal{V}_{candidate}^{{}^{\prime}}$ in the absence of an input image, representing tokens based on the MLLM’s inherent knowledge. We calculate that the overlap rate of $x_{h}$ existing in $\mathcal{V}_{candidate}^{{}^{\prime}}$ reaches 91.05%, suggesting that even 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.

## 3 Proposed Approach: Dynamic Correction Decoding with Preceding-Layer Knowledge

Figure: 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.
Refer to caption: x5.png

After investigating the reasons why MLLMs generate non-existent objects, inspired by (Chuang et al., ), we introduce Dynamic Correction Decoding with preCeding-Layer Knowledge (DeCo), which can alleviate hallucinations during inference.
The overall framework of Deco is illustrated in Figure 4, consisting of dynamic preceding layer selection (Section 3.1) and decoding correction with preceding-layer knowledge (Section 3.2).

### 3.1 Dynamic Preceding-Layer 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 et al., ), we use a truncation strategy to select the candidate tokens, with the default truncation strategy being top-$p$ truncation, formally:

$$ $\mathcal{V}_{\text{candidate}}\left(x_{T}|x_{<T}\right)=\left\{x_{T}\in \mathcal{V}:\sum_{v\in\mathcal{V}p}P_{\tau}(x_{T}=v|x_{0},x_{1},\dots,x_{T-1}) \leq p\right\}$ (3) $$

where $\mathcal{V}$ is the whole vocabulary, and $p$ refers to the parameter used in top-$p$.

**Table 1: Hit Rate of layers across different intervals.**
| Layer Range | 20-28 | 15-28 |
| --- | --- | --- |
| Hit Rate (%) | 61.69 | 71.14 |

#### Preceding-layer selection.

Our findings in Section 2 demonstrate that activated ground truth tokens typically exhibit higher probabilities in preceding layers compared to hallucinated tokens. Based on this observation, we hypothesize that selecting the token $x_{th},\text{where}~{}x_{th}\in\mathcal{V}_{candidate}$, with the highest probability from the interval layers corresponds to the ground truth token. We compute the accuracy of $x_{th}$ as the ground truth token and denote this metric as the hit rate, as shown in Table 1. The results indicate that within a specific range of layers (e.g., 15-28), $x_{th}$ indeed has a high universal probability of representing 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 MLLM. The selected preceding layer is referred to as the anchor layer, formally defined as:

$$ $\mathcal{A}=\text{argmax}_{i}{\left\{x_{T}\in\mathcal{V}_{\text{candidate}}: \text{softmax}(\phi(h_{T-1}^{i}))_{x_{T}},i\in\mathcal{[}a,b]\right\}},$ (4) $$

where $a\leq b,a,b\in[1,N]$, 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 $a=20$ and $b=28$ for our subsequent experiments.

### 3.2 Decoding Correction with Preceding-layer Knowledge

#### Dynamic soft modulation.

We introduce a dynamic modulation coefficient, defaulting to the maximum probability.
Formally, we have:

$$ $\text{max_prob}=\text{max}(\text{softmax}(\phi(h_{T-1}^{\mathcal{A}}))).$ (5) $$

This coefficient can help prevent hard changes in logits, particularly when the probability differences between candidate tokens in preceding layers are insignificant.
From the example in Figure 4, we 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, we integrate information from these layers into the final layer to correct the logit distribution.
We utilze a hyperparameter, $\alpha$, to 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-layer representations, the probability of predicting the next token and the logits are updated as follows:

$$ $\displaystyle\hat{p}(x_{T}\mid x_{<T})=\mathrm{softmax}\bigl{(}\text{logits} \bigr{)}_{x_{T}},\quad$ (6) $\displaystyle\text{logits}=\phi(h_{T-1}^{N})+\alpha\times\text{max_prob} \times\phi(h_{T-1}^{\mathcal{A}}),$ (7) $$

where $N$ is the last layer of MLLM and $\mathcal{A}$ is the selected preceding layer.

#### Comparison of previous methods.

Our work shares a similar assumption with OPERA (Huang et al., ) and VCD (Leng et al., ), 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., ) and VCD (Leng et al., ).
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 et al., ; Chen et al., ).
However, our method is actually parallel to previous approaches and can be combined to achieve better results.

## 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 baselines for mitigating hallucinations, as outlined below:
Dola (Chuang et al., ) is specifically designed for alleviating hallucinations in factual tasks for LLMs by reducing shallow semantic influences to improve the factuality of the final layer’s output.
VCD (Leng et al., ) mitigates the influence of 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., ) dynamically penalizes overconfident tokens based on the emergence of aggregation patterns, while proposing a retrospective allocation strategy to avoid cases where hallucinations have already occurred.
For all the baselines, we use the default hyperparameters from the source code for a fair comparsion.

Model.
We select four of the most representative MLLM models for evaluation, including InstructBLIP (Dai et al., ), MiniGPT-4 (Zhu et al., ), LLaVA-1.5 (Liu et al., ) and Qwen-VL  (Bai et al., ).
All the MLLMs we used have a 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 a 7B-sized, 32-layer decoder-only architecture language model, we choose layers 20-28 as candidates for the preceding layers (according to the findings in Section 2.1). For the image captioning and VQA tasks, $\alpha$ 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.

**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.**
| Decoding | Method | InstructBLIP | MiniGPT-4 | LLaVA-1.5 | Qwen-VL |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ |  |  |
| Greedy | Vanilla | 58.8 | 23.7 | 31.8 | 9.9 | 45.0 | 14.7 | 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 $\downarrow$17.6 | 14.4 $\downarrow$9.3 | 27.0 $\downarrow$4.8 | 8.8 $\downarrow$1.1 | 37.8 $\downarrow$7.2 | 11.1 $\downarrow$3.6 | 42.2 $\downarrow$3.8 | 10.7 $\downarrow$1.8 |
| Beam Search | Vanilla | 55.6 | 15.8 | 30.6 | 9.5 | 48.8 | 13.9 | 41.8 | 10.8 |
| OPERA | 46.4 | 14.2 | 26.2 | 9.5 | 44.6 | 12.8 | 34.6 | 9.5 |  |
| DeCo (Ours) | 43.8 $\downarrow$11.8 | 12.7 $\downarrow$3.1 | 24.8 $\downarrow$5.8 | 7.5 $\downarrow$2.0 | 33.0 $\downarrow$15.8 | 9.7 $\downarrow$4.2 | 32.0 $\downarrow$9.8 | 8.7 $\downarrow$2.1 |  |
| Nucleus | Vanilla | 54.6 | 24.8 | 32.6 | 10.7 | 48.8 | 14.2 | 49.2 | 13.1 |
| VCD | 58.0 | 17.0 | 33.8 | 11.1 | 54.0 | 16.0 | 46.4 | 11.9 |  |
| DeCo (Ours) | 43.6 $\downarrow$11.0 | 12.9 $\downarrow$11.9 | 30.8 $\downarrow$1.8 | 9.5 $\downarrow$1.2 | 42.8 $\downarrow$6.0 | 13.2 $\downarrow$1.0 | 43.8 $\downarrow$5.4 | 11.8 $\downarrow$1.3 |  |

### 4.2 Benchmark and metrics

CHAIR. Caption Hallucination Assessment with Image Relevance (CHAIR) (Rohrbach et al., ) 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 ($\mathrm{CHAIR_{I}}$) and sentence level ($\mathrm{CHAIR_{S}}$), as shown in Eq. 8.
Following (Huang et al., ), 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.”.

$$ $\displaystyle\mathrm{CHAIR_{I}}=\frac{|\{\text{hallucinated objects}\}|}{\text {all mentioned objects}},\mathrm{CHAIR_{S}}=\frac{|\{\text{captions with hallucinated objects}\}|}{\text{all captions}}.$ (8) $$

**Table 3: POPE hallucination evaluation results. The best results are in bold.**
| Decoding | Method | InstructBLIP | MiniGPT-4 | LLaVA-1.5 | Qwen-VL |
| --- | --- | --- | --- | --- | --- |
| $\mathrm{F1}\uparrow$ | $\mathrm{F1}\uparrow$ | $\mathrm{F1}\uparrow$ | $\mathrm{F1}\uparrow$ |  |  |
| Greedy | Vanilla | 80.0 | 58.5 | 82.2 | 85.2 |
| DoLa | 83.4 | 72.8 | 83.2 | 85.8 |  |
| DeCo (Ours) | 84.9 $\uparrow$4.9 | 77.4 $\uparrow$18.9 | 86.7 $\uparrow$4.5 | 86.3 $\uparrow$1.1 |  |
| Beam Search | Vanilla | 84.4 | 70.3 | 84.9 | 85.3 |
| OPERA | 84.8 | 73.3 | 85.4 | 86.1 |  |
| DeCo (Ours) | 84.9 $\uparrow$0.5 | 77.9 $\uparrow$7.6 | 86.7 $\uparrow$1.8 | 86.4 $\uparrow$1.1 |  |
| Nucleus | Vanilla | 79.8 | 52.8 | 83.1 | 84.5 |
| VCD | 79.9 | 56.0 | 83.1 | 84.7 |  |
| DeCo (Ours) | 81.8 $\uparrow$2.0 | 63.8 $\uparrow$11.0 | 85.4 $\uparrow$2.3 | 85.2 $\uparrow$0.7 |  |

POPE. The Polling-based Object Probing Evaluation (POPE) (Li et al., ) is a VQA-based metric for assessing object hallucination in MLLMs.
It evaluates hallucinations by asking questions such as “Is there a <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 F1 score for performance evaluation.

MME. The comprehensive MLLM Evaluation benchmark (MME)  (Fu et al., ) assesses the perceptual and cognitive abilities of MLLMs across a total of 14 subtasks, including tasks such as OCR, visual knowledge, attribute relationships, and object recognition.

GPT-4o assisted evaluation.
To further assess the model’s performance in image captioning, we extend beyond the CHAIR metric, which targets object hallucination.
Following prior studies (Huang et al., ; Leng et al., ), an open evaluation is conducted using GPT-4o on 100 randomly sampled COCO images.
GPT-4o assesses two assistants’ descriptions in terms of Accuracy (C) and Detailedness (D).
We introduce the prompt used in the experiments in Table 8.

### 4.3 Experimental Results

Results of hallucination in image captioning.
Note that we use the baseline’s original decoding settings for a fair comparison and run DeCo under the same settings.
From Table 2, we notice that DeCo consistently outperforms other approaches in mitigating hallucinations across four MLLMs—InstructBLIP, MiniGPT-4, LLaVA-1.5, and Qwen-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).
Additionally, VCD does not perform as well, likely due to producing an increased number of hallucinated descriptions during the generation process.
In conclusion, the proposed approach DeCo effectively reduces hallucinations in visual description tasks solely through dynamic decoding correction, achieving an average suppression rate of approximately 10.8% on image captioning datasets.
Additionally, we further evaluate the performance of DeCo on the AMBER image caption dataset, as detailed in Table 7 of the Appendix.

Figure: Figure 5: DeCo generally improves the MLLM’s performance.
Refer to caption: x6.png

Results of hallucination in VQA.
In contrast to image captioning, POPE employs a simple polling approach to assess hallucination levels in MLLMs with respect to object recognition.
As shown in Table 3, DeCo demonstrates superior performance across all settings, further validating the effectiveness of the proposed approach.
Additionally, Figure 5 reveals that DeCo also achieves better results on MME, which evaluates the multifaceted VQA capabilities of LLaVA-1.5.
These findings suggest that the underlying mechanism we identified not only applies to object recognition but also extends to attribute-related tasks and more complex reasoning tasks.

Results of GPT-4o’s assistance.
Following (Huang et al., ; Leng et al., ), we further use GPT-4o to evaluate our method against greedy decoding across four distinct models.
From Table 4, we notice that our approach consistently outperform greedy decoding in terms of accuracy, demonstrating 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 to greedy decoding.
Nonetheless, our method exhibit a clear advantage in mitigating hallucinations across all evaluated models.

**Table 4: GPT-4o assisted hallucination evaluation results on MSCOCO. Two aspects are verified, correctness ($C$) and detailedness ($D$).**
| Method | InstructBLIP | MiniGPT-4 | LLaVA-1.5 | Qwen-VL |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $C$ | $D$ | $C$ | $D$ | $C$ | $D$ | $C$ | $D$ |  |
| Greedy Search | 4.92 | 5.65 | 5.71 | 6.20 | 5.21 | 6.31 | 5.56 | 6.62 |
| DeCo (Ours) | 6.25 | 5.77 | 6.33 | 6.08 | 7.42 | 6.25 | 7.81 | 6.70 |

### 4.4 Analysis

Figure: Figure 6: Comparison of latency and throughput across different baselines.
Refer to caption: x7.png

#### 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 comparison.
The findings indicate that DeCo operates within an acceptable efficiency cost, striking a balance between effectiveness and computational overhead.
Compared to the basic decoding process, the latency increase introduced by our 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 evaluate 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 5 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 effectiveness of our proposed method.

**Table 5: Comparison of results between DeCo and perturbed DeCo in image captioning tasks**
| Method | InstructBLIP | MiniGPT-4 | LLaVA-1.5 | Qwen-VL |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ | $\mathrm{CHAIR_{S}}\downarrow$ | $\mathrm{CHAIR_{I}}\downarrow$ |  |
| DeCo | 39.3 | 12.6 | 32.4 | 9.6 | 38.8 | 11.1 | 44.5 | 11.1 |
| DeCo + $\epsilon$ | 45.6 $\uparrow$6.3 | 14.3 $\uparrow$1.7 | 33.3 $\uparrow$0.9 | 10.1 $\uparrow$0.5 | 42.2 $\uparrow$2.4 | 11.3 $\uparrow$0.2 | 47.0 $\uparrow$2.5 | 12.8 $\uparrow$1.7 |

#### Hyperparameter analysis.

Our method incorporates two primary hyperparameters: $\alpha$ and the selection of interval layers.
In the experiments, we employ DeCo based on greedy decoding.
On the one hand, the hyperparameter $\alpha$ regulates the intensity of early information enhancement.
Figure 7(a) illustrates the performance across various $\alpha$ values.
We observe that hallucination suppression is most effective when $\alpha$ approximates 0.6.
As $\alpha$ increases, the efficacy of DeCo in mitigating hallucinations improves.
However, it is crucial to note that excessively high $\alpha$ values may lead to the generation of atypical image descriptions, characterized by repetitive word usage.
On the other hand, the layer interval hyperparameter $\mathcal{[}a,b]$ determines the candidate layers for inclusion in the enhancement process.
We conduct experiments using intervals of four layers, with results presented in Figure 7(b).
Our analysis reveals that hallucination suppression 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 in Section 2.2.

Figure: (a) Ablation study of $\alpha$.
Refer to caption: x8.png

#### Mitigating snowballing hallucinations.

Snowballing hallucinations are a prevalent issue in the responses generated by MLLMs.
This phenomenon occurs when an initial hallucination triggers a sequence of subsequent errors, leading to a compounding effect that significantly degrades the quality and coherence of the generated text. Figure 8 illustrates a typical example of snowballing hallucinations, where an initial misinterpretation of the visual input propagates through the decoding process, resulting in a highly inconsistent and erroneous output.
Our approach can reduce the accumulation 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 9, 10, 11, and 12 in Appendix C.

## 5 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., ; Chen et al., ).
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 MLLMs.
Grad-CAM (Zhang et al., ) visualizations reveal that image-text interaction exists in the preceding layers (1-11) but not in the deep layers.
OPERA (Huang et al., ) further proposes that the “Aggregation Pattern” leads to hallucination, 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 to a 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 information.
VCD (Leng et al., ) discovers that MLLMs generate high-confidence answers even when the image is noisy or absent, indicating a bias towards LLM priors.
Similarly, PAI (Liu et al., ) 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 MLLMs

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., ; Gunjal et al., ; Chen et al., ) and alignment training (Sun et al., ; Yu et al., ; Chen et al., ; Li et al., ) 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 additional tools or self-reflection strategies to revise the response.
For instance, LURE (Zhou et al., ) detects hallucinations using manually-crafted features and revises the generated text accordingly.
Woodpecker (Yin et al., ) combines MLLM outputs with an expert VQA model to post-edit hallucinations.
VOLCANO (Lee et al., ) trains MLLMs to provide self-feedback and reflect on the original generated text.
However, these methods incur additional inference costs and delays, and require task-specific procedures and prompts to be designed (Xu et al., ).
Training-free decoding methods have been explored to mitigate hallucination.
OPERA (Huang et al., ) identifies an abnormal attention pattern that often accompanies hallucinated descriptions and proposes the mitigation method based on this pattern. VCD (Leng et al., ) introduces the notion that visual uncertainty increases hallucination and proposes a contrast decoding method to alleviate the issue.
VDD (Zhang et al., ) proposes a “Post-Hoc debias” approach that ensures uniform scores for each answer in the absence of an image to mitigate the influence of LLM priors.

## 6 Conclusion and Limitations

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 on this insight, we introduce DeCo, dynamic correction decoding with preceding-layer knowledge to mitigate hallucinations.
Extensive experiments demonstrate the efficacy of our approach, which also shows advantages in latency and throughput.

#### Limitations.

(1) Lack of generalized research. Due to the GPU cost consideration, we conduct experiments solely on limited MLLMs, without exploring additional MLLMs or those with larger parameter sizes.
(2) No free lunch. The results shown in Table 4 indicate that our method has a 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.

## 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 A.1 and A.2 provides a detailed description of the experimental setup for the mechanism experiments.

## Appendices

## Appendix A Detailed Experimental Setup

### A.1 Detailed Settings for Findings 1

In the probing experiment, we utilize the pipeline proposed in the POPE (Li et al., ) to construct 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 or non-existence. (Note: There is no overlap between the training data and the evaluation data for object hallucination proposed by the POPE).
We select the AMBER dataset (Wang et al., ), which has a different distribution from the MSCOCO dataset, to test whether our conclusions can generalize. The AMBER dataset contains 1,004 carefully annotated images, each labeled with existent objects as well as non-existent 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-distribution (OOD).

Previous work (Karamcheti et al., ) has demonstrated that increasing the resolution of the vision encoder enhances the visual comprehension capabilities of MLLMs. In our study, we compare LLaVA trained with a 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, although both initial models are based on Vicuna-1.5-7b. Our results, as illustrated in the Figure 1(b), further affirm the scaling law associated with visual resolution, while also providing indirect validation of the reliability of the probing experiments.

### A.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 preceding text. Specifically, for the sentence “Additionally, there is a car.”, we extract the hallucinated object token “car” and the preceding text “Additionally, there is a”.
We re-input the preceding text into the MLLM and observe the changes in its internal state when predicting the next token. We denote that a total of $K$ preceding texts are selected, with the j-th preceding text denoted as $s^{j}$.

**Table 6: Randomly prompts.**
| Prompts |
| --- |
| Describe the image. |
| Please describe this image in detail. |
| Generate a caption for this image. |

## Appendix B 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, as well as some potential 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 MLLMs, while a higher Cover value signifies better diversity. We compare Deco with the baselines on the LLaVA-1.5-7b. The results are as shown in Table 7. The results reveal that Deco demonstrates a significant advantage in truthfulness, although its diversity is somewhat lacking, yet remains within an acceptable range.

**Table 7: Results of using DeCo on the AMBER image caption dataset with LLaVA-1.5-7b.**
| Decoding | Method | LLaVA-1.5 |  |  |  |
| --- | --- | --- | --- | --- | --- |
| $\mathrm{CHAIR}\downarrow$ | $\mathrm{Cover}\uparrow$ | $\mathrm{Hal}\downarrow$ | $\mathrm{Cog}\downarrow$ |  |  |
| Greedy | Vanilla | 8.2 | 48.9 | 34.3 | 4.0 |
| DoLa | 8.0 | 50.8 | 37.5 | 4.3 |  |
|  | DeCo (Ours) | 6.6 $\downarrow$1.6 | 47.5 $\downarrow$1.4 | 28.1 $\downarrow$6.2 | 2.8 $\downarrow$1.2 |
| Beam Search | Vanilla | 7.1 | 50.7 | 32.4 | 3.8 |
| OPERA | 6.4 | 49.0 | 27.5 | 2.9 |  |
| DeCo (Ours) | 6.3 $\downarrow$0.8 | 46.8 $\downarrow$3.9 | 25.1 $\downarrow$7.3 | 2.4 $\downarrow$1.4 |  |
| Nucleus | Vanilla | 10.2 | 50.2 | 43.3 | 4.5 |
| VCD | 9.0 | 51.7 | 40.2 | 4.4 |  |
| DeCo (Ours) | 8.3 $\downarrow$1.9 | 48.0 $\downarrow$2.2 | 37.5 $\downarrow$5.8 | 3.4 $\downarrow$1.1 |  |

## Appendix C Case Analysis Across Diverse MLLMs

Figure: Figure 8: The case of mitigating snowballing hallucination with DeCo.
Refer to caption: x10.png

Figure: Figure 9: DeCo’s performance in reducing hallucinations of InstructBlip-7B on three basic decoing methods.
Refer to caption: x11.png

Figure: Figure 10: DeCo’s performance in reducing hallucinations of LLaVA-1.5-7B on three basic decoing methods.
Refer to caption: x12.png

Figure: Figure 11: DeCo’s performance in reducing hallucinations of Qwen-VL-7B on three basic decoing methods.
Refer to caption: x13.png

Figure: Figure 12: DeCo’s performance in reducing hallucinations of MiniGPT4-7B on three basic decoing methods.
Refer to caption: x14.png

**Table 8: The prompt used for GPT-4o evaluation adopted from Leng et al. (); Huang et al. (); Liu et al. ()**
| GPT-4o Prompt |
| --- |
| You are required to score the performance of two AI assistants in describing a given image. You should pay extra attention to the hallucination, which refers to the part of descriptions that are inconsistent with the image content, such as claiming the existence of something not present in the image or describing incorrectly in terms of the counts, positions, or colors of objects in the image. Please rate the responses of the assistants on a scale of 1 to 10, where a higher score indicates better performance, according to the following criteria:<br>1: Accuracy: whether the response is accurate with respect to the image content. Responses with fewer hallucinations should be given higher scores.<br>2: Detailedness: whether the response is rich in necessary details. Note that hallucinated descriptions should not count as necessary details.<br>Please output the scores for each criterion, containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. Following the scores, please provide an 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 1]<br>{Response of Assistant 1}<br>[End of Assistant 1] |
| [Assistant 2]<br>{Response of Assistant 2}<br>[End of Assistant 2] |
| Output format:<br>Accuracy: <Scores of the two answers><br>Reason: |
| Detailedness: <Scores of the two answers><br>Reason: |

## Peer Reviews

confidence: 5
contribution: 3
initial_rating: 6
presentation: 4
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?
- 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?
soundness: 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.
- The figures illustrating token probabilities across transformer layers effectively highlight the trends for hallucinated versus non-hallucinated tokens, making the analysis accessible and informative.
- 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.
- 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.
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.
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.
- 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.
- 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.

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
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.
soundness: 3
strengths: 1. The motivation seems interesting.
2. The paper is well written and easy to follow. The diagrams are essential to understanding this paper.
3. This paper achieves good results on existing datasets.
4. The main technical pipeline is clear.
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.
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?
2. 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?
3. Could you provide more evidence to demonstrate how dynamic soft modulation prevents abrupt changes in logits? Additional ablation studies might further substantiate this claim.
4. Could you share detailed MME results to highlight the method's performance across different subtasks?

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
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?
- POPE evaluates answers in Yes/No. How could decoding strategy have impact on the performance for this benchmark?
soundness: 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
- The proposed mitigation method is lightweight and efficient.
- The experimental results are in general better than baselines.
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.
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. 

There 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. 

In 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.

The 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.

I 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. 

**Reference**

[1] Liu, Junteng, et al. "On the Universal Truthfulness Hyperplane Inside LLMs." arXiv preprint arXiv:2407.08582 (2024).

[2] Li, Kenneth, et al. "Inference-time intervention: Eliciting truthful answers from a language model." Advances in Neural Information Processing Systems 36 (2024).

[3] Zhang, Tianhang, et al. "Enhancing uncertainty-based hallucination detection with stronger focus." arXiv preprint arXiv:2311.13230 (2023).

[4] Azaria, Amos, and Tom Mitchell. "The internal state of an LLM knows when it's lying." arXiv preprint arXiv:2304.13734 (2023).

[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).

[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.

---

confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
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.
- 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?
-  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?
- Is this decoding method useful in more advanced VLLMs, such as Qwen-VL, VILA, etc.?
soundness: 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.

-  The introduction of DeCo is innovative, using preceding-layer knowledge to reduce hallucinations during inference, effectively improving output accuracy.

- The method of probing across transformer layers reveals how hallucinations emerge in later layers, helping to understand MLLM behavior better.
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.
If these concerns are addressed, I will consider raising my score.
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. 
- 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.

Meta-review: This works shows that though MLLMs could generate incorrect outputs in the final layer, they could effectively recognise objects in preceding layers. Thus, this work introduces a dynamic correction decoding approach for MLLMs to adaptively choose relevant layers and integrate their knowledge into the final layer to adjust outputs. This insight provides a new layer-wise view on the hallucination problem in MLLMs. The proposed method is lightweight and efficient, and the results show the efficacy of such a method. All the reviewers recommend acceptance. In the camera ready version, authors need to carefully improve the paper following reviewers suggestions, including adding in-depth analysis of the model and statistical analysis.

Decision: Accept (Poster)