3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models
Authors: Zhaoxi Zhang, Sanwoo Lee, Zhixiang Wang, Yunfang Wu
Accepted at: acl25

3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models

Zhaoxi Zhang1,2,4, Sanwoo Lee1,2, Zhixiang Wang1,3, Yunfang Wu1,2*
1National Key Laboratory for Multimedia Information Processing, Peking University
2School of Computer Science, Peking University, Beijing, China
3School of Software and Microelectronics, Peking University, Beijing, China
4School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China
{1120210536}@bit.edu.cn, {sanwoo}@pku.edu.cn, {ekko}@stu.pku.edu.cn, {wuyf}@pku.edu.cn

Abstract

The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications. The code of this paper can be found at https://github.com/JesseZZZZZZ/3DM.

<table>
  <tr>
    <th>Model</th>
    <th>Acc</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1</th>
  </tr>
  <tr>
    <td>LLaVA-v1.5-7b</td>
    <td>0.516</td>
    <td>0.469</td>
    <td>0.947</td>
    <td>0.628</td>
  </tr>
  <tr>
    <td>ChatGLM4-9b</td>
    <td>0.689</td>
    <td>0.725</td>
    <td>0.450</td>
    <td>0.555</td>
  </tr>
</table>

Table 1: The performance of LLaVA-v1.5-7b with a positive bias, and ChatGLM4-9b with a negative bias.

![Conceptual comparison of model merging with fine-tuning and ensembling in the context of debiasing. Model merging is training-free and benefits from efficient inference.](page_1012_1012_388_180.png)

Figure 1: Conceptual comparison of model merging with fine-tuning and ensembling in the context of debiasing. Model merging is training-free and benefits from efficient inference.

Recent advances in MLLMs (Liu et al., 2023; Chen et al., 2024; GLM et al., 2024; Zhu et al., 2024a) have shown remarkable performance in various multimodal tasks, ranging from image captioning (Wang et al., 2024) and visual question answering (Li et al., 2023) to a nuanced multimodal sarcasm detection (Tang et al., 2024). Despite the progress, MLLMs are prone to biased predictions (Cui et al., 2023; Han et al., 2024). For instance, Table 1 shows that LLaVA (Liu et al., 2023) favors classifying inputs as sarcastic (positive-biased model), whereas ChatGLM (GLM et al., 2024) has the opposite tendency (negative-biased model). This may be a symptom of hallucinating answers from spurious correlations seen in the dataset (Bai et al., 2024). MLLMs’ inherent biases compromise their reliability and fairness for deployment in real-world applications. Thus, enhancing MLLMs’ accuracy and ensuring minimal bias have significant practical implications.

In this paper, we present the first attempt, to the best of our knowledge, at merging models (Yang et al., 2024; Ramé et al., 2023; Lin et al., 2024) to debias MLLMs and showcasing its general effectiveness. Existing debiasing or dehallucination methods have relied on labeled datasets for fine-tuning (Chen et al., 2021; Guo et al., 2022; Liu et al., 2024) or repetitive inference for ensembling predictions (Clark et al., 2019), both of which in-
cur non-trivial computational overhead. In contrast, our approach collects a positive-biased model and a negative-biased model, then merges them in the parameter space without the need for additional training and repeated inference. Through this process, biases in opposite directions are canceled out efficiently. See Fig. 1 for the conceptual comparisons between merging and the traditional approaches.

However, merging MLLMs for debiasing faces several challenges: (1) Merging models often requires the same architecture across models to allow for parameter-wise operations, a condition rarely satisfied in the rapidly evolving ecosystem of MLLMs (Zhang et al., 2024); (2) Reducing the bias alone does not always translate to improved accuracy—debiasing models may struggle with task performance. This highlights the need to refine existing merging methods (Ilharco et al., 2022; Yadav et al., 2024; Yu et al., 2024) through the lens of reducing bias and enhancing accuracy.

We propose 3DM (Distill, Dynamic Drop and Merge), an architecture-agnostic merging framework designed to address these challenges. First, knowledge distillation (Gou et al., 2021) bridges architectural gaps between models, enabling parameter-level merging even for heterogeneous MLLMs. Second, we introduce a dynamic dropping strategy that assigns parameter-specific drop rates based on their influence on bias and accuracy. This is motivated by a recent merging method—DARE (Yu et al., 2024)—that sparsifies parameters by a uniform chance of dropout and treats all parameters equally.

We first conduct experiments on the MMSD2.0 (Qin et al., 2023) sarcasm detection dataset and measure models’ bias with our newly proposed metric, Bias Ratio (Sec. 3). The results demonstrate that (1) merging methods are in common effective in reducing bias, and that (2) 3DM significantly outperforms DARE and other baselines in accuracy, F1-score, and Bias Ratio. In addition, experiments on MMSD1.0 (Cai et al., 2019) further validate that 3DM generalizes well across different datasets. Compared with methods requiring hyper-parameter search over the validation data, 3DM does not contain such hyperparameters, making it convenient for implementation.

In essence, our contributions are as follows:

1. Architecture Alignment: A distillation pipeline that aligns MLLM architectures, preserving their original bias and accuracy.

2. Dynamic Dropping: A merging strategy that adaptively adjusts drop rates to reduce biases and improve accuracy.

3. Bias Ratio: A metric for quantifying bias direction and magnitude, contributing to ongoing efforts in bias quantification.

4. Empirical Validation: Extensive experiments demonstrating 3DM’s effectiveness in terms of both debiasing and accuracy enhancement.

2 Related Work

2.1 Model Debiasing

Existing debiasing mechanisms in the literature can be classified into two primary categories (Mehrabi et al., 2021; Pessach and Shmueli, 2022): training-based debiasing and training-free debiasing. Training-based debiasing approaches necessitate modifications to the training dataset (Li and Vasconcelos, 2019), demonstrating notable effectiveness while requiring extensively annotated training data. Conversely, training-free debiasing methodologies primarily focus on altering the output distribution (Kamiran et al., 2012), with ensembling emerging as a crucial technique in this domain (Clark et al., 2019).

A notable example of ensembling is the blindfolding strategy proposed by Zhu et al. (2024b), which involves masking specific portions of the input and computing the final output score as the difference between traditional inference, fully blindfolded inference, and partially blindfolded inference. Although ensembling methods eliminate the need for training processes, they incur substantial computational overhead due to the requirement for multiple inference operations. In light of these considerations, we propose our merging strategy as an effective compromise between these two approaches, offering the dual advantages of eliminating excessive inference requirements while maintaining a label-free training process.

2.2 Model Merging

Garipov et al. (2018); Draxler et al. (2018) demonstrated that two models trained from different initializations can be connected by a path of non-increasing loss in the loss landscape, referred to as model connectivity. If the two models share a significant part of the optimization trajectory (e.g., pre-trained model), they are often connected by a
linear path (Frankle et al., 2020; Neyshabur et al., 2020; Mirzadeh et al., 2021), where interpolating along the path potentially leads to better accuracy and generalization (Izmailov et al., 2018). This property has been exploited as simply averaging the weights of numerous models fine-tuned from different hyperparameters to improve accuracy (Wortsman et al., 2022), popularizing model merging as an efficient alternative to ensemble in combining models without additional instruction tuning.

The success of averaging fine-tuned models has led to a surge of merging methods, aimed at steering models’ behavior in desired way. A prominent example is multi-task learning via merging, where accounting for parameter importance (Matena and Raffel, 2022) and minimizing prediction differences to the fine-tuned models (Jin et al., 2022) are shown to be effective. While these methods relies on statistics that are expensive to compute, Task Arithmetic (Ilharco et al., 2022) presents a cost-effective and scalable method of adding the weighted average of task vectors (i.e., fine-tuned part of parameters) to the pre-trained model. Subsequent studies are dedicated to pre-processing task vectors to reduce interference across models (Yadav et al., 2024; Yu et al., 2024; Deep et al., 2024). Moreover, distillation is proposed for architecture alignment by FUSECHAT (Wan et al., 2024). Our distill-merge pipeline and dynamic dropping strategy aligns with this line of research, however we are focused on editing task vectors to reduce bias and improve accuracy.

3 Bias Ratio

The metrics used to evaluate a model’s bias (or fairness) remain a subject of ongoing dialogue, with no clear consensus yet (Caton and Haas, 2024). Previous studies have employed various evaluation metrics to assess bias. In this work, we introduce the Bias Ratio (BR) as a measure of a model’s bias, which is based on the quantities of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN).

\[
BR = \frac{FP}{FP + TN} - \frac{FN}{FN + TP}
\]

The Bias Ratio (BR) ranges from \(-1\) to \(1\), where its absolute value indicates the magnitude of bias, and its sign denotes the direction. For instance, a BR value of \(0.8\) reflects a relatively high degree of positive bias, whereas a BR of \(-0.1\) suggests a relatively low degree of negative bias. While previous studies have primarily conducted qualitative analyses of bias based on TP, TN, FP, and FN, we propose a quantitative metric to systematically assess both the degree and direction of bias.

4 Method

Focusing on a two-way classification task (e.g., sarcasm detection), suppose we are given two MLLMs, a positive-biased model and a negative-biased model: A positive-biased model tends to classify an input as positive sample, represented by high recall and low precision (Table 1). Likewise, a negative-biased model is inclined to classify an input as negative sample, represented by low recall and high precision (Table 1).

Then we apply our proposed 3DM framework following three steps, as illustrated in Fig. 2: (1) knowledge distillation for architecture alignment; (2) dynamic dropping strategy that filters out delta parameters based on the contribution to accuracy and bias; (3) merging the positive-biased delta parameters and negative-biased delta parameters to cancel out predictive bias.

4.1 Architecture Alignment via Distillation

An intuitive way to mitigate bias is to merge a positive-biased model and a negative-biased model to cancel out the bias. However, the diverse ecosystem of MLLMs makes it challenging to guarantee those two models to share the same architecture, blocking them from being merged through parameter-wise operations. Knowledge distillation provides a viable solution by reshaping the two models into the same architecture, while preserving the predictive accuracy and bias of each model. Hence we start by distilling the two types of models and proceed to model merging (Sec. 4.2, 4.3) on the basis of compatible architecture.

Knowledge distillation (Gou et al., 2021) typically follows a teacher-student structure, where the teacher model’s output (generated by the prompt proposed in Sec. 5.1.2) supervises the student model such that the student model inherits the behavior of the teacher model. Note that the student model is not required to be smaller than the teacher model in our case, as our goal of knowledge distillation does not lie in compression.

Specially, we fine-tune the pre-trained model using pseudo labels generated by a teacher model (i.e., either positive-biased model or negative-biased model). We minimize cross-entropy loss evaluated
Figure 2: Overview of 3DM framework. First, positive-biased model and negative-biased model are distilled to a base student model to share an identical architecture. Second, dynamic dropping assigns a drop rate to each delta parameter based on the discrepancy between the positive-biased model and the negative-biased model. Then, sparse task vectors after dropping are added to the base model to build a debiased model.

on the pseudo labels:

\[
\mathcal{L}_{ce} = - \sum_{t=1}^{m} \log P(\hat{y}_t \mid x, \hat{y}_{<t})
\]

where \( \{\hat{y}_t\}_{t=1}^m \) is the pseudo label of length \( m \) generated by teacher model. In the context of sarcasm detection, \( x \) is a pair of input text and image and \( \{\hat{y}_t\}_{t=1}^m \) is an answer sequence indicating whether the input pair contains sarcasm.

4.2 Dynamic Dropping

Merging a positive-biased model and a negative-biased model is in general effective in alleviating the bias. In this section, we further propose dynamic dropping, aiming to improve accuracy and F1-score while simultaneously reducing bias.

In model merging, delta parameters are defined as the subtraction of parameters of base model from the fine-tuned model, and they can be understood as task vectors (Ilharco et al., 2022). Findings by Yu et al. (2024) suggest that one could randomly zero-out delta parameters of an LLM with a drop rate of \( p \) and re-scale the remaining ones by \( 1/(1-p) \) without impacting the model’s performance. This sparsification strategy—coined as DARE—has been shown to be helpful in reducing parameter interference among the models to be merged. However, DARE assigns the same drop rate for all delta parameters.

Conversely, the drop rate of a delta parameter should ideally be determined by its contribution to improving accuracy and reducing bias. That is, "important" delta parameters should be preserved by a higher probability.

Delta Parameters We merge the distilled positive-biased model and negative-biased model by editing their respective delta parameters and combining those to the base student model. Delta parameters are defined as:

\[
d_{ij}^P = W_{ij}^P - W_{ij}^{base}
\]
(3)

\[
d_{ij}^N = W_{ij}^N - W_{ij}^{base}
\]
(4)

where \( W^{base} \in \mathbb{R}^{m \times n} \) is a parameter matrix of the base model and \( W^P \) and \( W^N \) are the ones distilled from positive-biased model and negative-biased model, respectively. \( i \) and \( j \) denotes position \((i, j)\) of the parameter in \( W \).

Classification of Delta Parameters. In terms of which delta parameters are more responsible for boosting model’s accuracy and suppressing bias, we suggest the following criteria for classifying delta parameters into three categories:

1. Bias-free Delta (Fig. 3(a)), where \( d_{ij}^P \) and \( d_{ij}^N \) have the same sign, i.e. \( d_{ij}^P d_{ij}^N > 0 \).

2. Unidirectional Delta (Fig. 3(b)), where \( d_{ij}^P \) and \( d_{ij}^N \) have the opposite sign, and the magnitude of one dominates the magnitude of the other, i.e. \( d_{ij}^P d_{ij}^N < 0 \) and \( |d_{ij}^P + d_{ij}^N| > c \) where \( c \) is a threshold.
Figure 3: Configurations of delta parameters under different conditions. The delta parameter from the positive-biased model (blue) and the negative-biased model (pink) can exhibit (a) the same sign, (b) opposite signs with a large magnitude difference, or (c) opposite signs with comparable magnitudes (dashed).

3. Bidirectional Delta (Fig. 3(c)), where \( d_{ij}^P \) and \( d_{ij}^N \) have the opposite signs, and the magnitudes of both are comparable, i.e. \( d_{ij}^P d_{ij}^N < 0 \) and \( |d_{ij}^P + d_{ij}^N| < c \).

The above criteria follows from our hypothesis about the roles of delta parameters: (1) Delta parameters with the same sign indicates a consistent direction in parameter updates by the positive-biased model and negative-biased model, potentially implying salient deltas that are associated with accuracy; (2) Given that positive-biased model and negative-biased model are best distinguished by their bias, those delta parameters with the opposite sign have greater contribution to bias, in which bidirectional delta may lead to severer interference while merging than unidirectional delta.

Towards Adaptive Drop Rate via Dynamic Dropping. Our classification of delta parameters motivates us to assign increasing drop rates for bias-free delta, unidirectional delta, and bidirectional delta. In light of this, we present dynamic dropping, a strategy of applying adaptive drop rate \( p_{ij} \) at a parameter-level:

\[
p_{ij} = \begin{cases}
0 & \text{if } d_{ij}^P d_{ij}^N \geq 0 \\
1 - \frac{|d_{ij}^P + d_{ij}^N|}{|d_{ij}^P| + |d_{ij}^N|} & \text{if } d_{ij}^P d_{ij}^N < 0
\end{cases}
\]

Here, \( p_{ij} \) is the drop rate between 0 and 1. Intuitively, Eq. 5 excludes bias-free delta from dropout operation, and for \( d_{ij}^P d_{ij}^N < 0 \), Eq. 5 imposes higher drop rate on bidirectional delta than on unidirectional delta. Noted, we implement a synchronized dropping mechanism where delta parameters at the same position are either dropped or retained simultaneously.

<table>
  <tr>
    <th>MMSD1.0</th>
    <th>All</th>
    <th>Positive</th>
    <th>Negative</th>
  </tr>
  <tr>
    <td>Train</td>
    <td>19816</td>
    <td>8642</td>
    <td>11174</td>
  </tr>
  <tr>
    <td>Validation</td>
    <td>2410</td>
    <td>959</td>
    <td>1451</td>
  </tr>
  <tr>
    <td>Test</td>
    <td>2409</td>
    <td>959</td>
    <td>1450</td>
  </tr>
</table>

Table 2: Composition of MMSD1.0 dataset.

<table>
  <tr>
    <th>MMSD2.0</th>
    <th>All</th>
    <th>Positive</th>
    <th>Negative</th>
  </tr>
  <tr>
    <td>Train</td>
    <td>19816</td>
    <td>9572</td>
    <td>10240</td>
  </tr>
  <tr>
    <td>Validation</td>
    <td>2410</td>
    <td>1042</td>
    <td>1368</td>
  </tr>
  <tr>
    <td>Test</td>
    <td>2409</td>
    <td>1037</td>
    <td>1372</td>
  </tr>
</table>

Table 3: Composition of MMSD2.0 dataset.

After dynamic dropping, each remaining delta parameter is rescaled by \( 1/(1 - p_{ij}) \) to preserve the expectation of input embeddings, as elaborated in Yu et al. (2024).

4.3 Parameter Merging

Let the delta parameters after dynamic dropping and re-scaling be \( \hat{d}_{ij}^P \) and \( \hat{d}_{ij}^N \). Then the average of \( \hat{d}_{ij}^P \) and \( \hat{d}_{ij}^N \) is added to the base model parameter to derive the merged parameter \( W_{ij}^* \):

\[
W_{ij}^* = 0.5 \hat{d}_{ij}^P + 0.5 \hat{d}_{ij}^N + W_{ij}^{base}
\]

\( W^* \) is the final model weights of our 3DM method, where bias is reduced and the overall performance is boosted.

5 Experiments

In this section, we first introduce the experimental setup, including the datasets, prompts, base models, and baselines. Then, we design experiments to validate our method. Distillation (5.2), merging (5.3), ensembling (5.5), and generalizability (5.6) are analyzed in this section.

5.1 Experimental Setup

5.1.1 Dataset

We validate our approach on MMSD2.0 (Qin et al., 2023), a multi-modal sarcasm detection dataset whose test set contains 2409 sentences along with images, and we test the generalizability on MMSD1.0 (Cai et al., 2019). See Table 3 for dataset statistics.

5.1.2 Implementation Details

Prompt Template. We use a fixed template to format the prompt. The template is carefully designed to ensure consistency across all samples
Figure 4: Visualization of the percentage of different types of parameters we encounter when merging. Blue and red represent the sign of \( d_{ij}^P \) and \( d_{ij}^N \) being same and different. The y axis represents the last y layers in the model.

and to minimize any potential bias introduced by expression. The following prompt template is used:

"<image>This is an image with: " as the caption. Is the image-text pair sarcastic? First answer the question with yes or no, then explain your reasons."

Knowledge Distillation. To examine the validity of knowledge distillation in transferring both accuracy and bias from the teacher models, we choose LLaVA-1.5-7b (Liu et al., 2023) and InternVL-2.5-8b (Chen et al., 2024) as student models (base models), and select LLaVA-1.5-7b and ChatGLM4-9b (GLM et al., 2024) as teacher models. Our choices of small-sized MLLMs are intended to show that 3DM does not necessitate any pre-existing sarcasm detection capabilities in the student models.

Dynamic Dropping. To assess the effectiveness of dynamic dropping, we fix InternVL as the base model and obtain positive and negative delta parameters distilled from LLaVA and ChatGLM, respectively. Choosing InternVL is informed by empirical observations (See Table 4), indicating that the pretrained InternVL exhibits weak bias (\( BR = 0.185 \)) relatively, and has no pre-existing knowledge of sarcasm detection (\( Acc \approx 0.5 \)), making it an appropriate candidate for our experiment.

Hyperparameter Searching The fixed drop rate of DARE and our ablation study (unused in 3DM) is set to 0.7, which is the result of tuning on the validation set of MMSD2.0 (Table 8).

5.1.3 Baselines
We compare 3DM with merging baselines including Average Merging (Wortsman et al., 2022), TIES (Yadav et al., 2024) and DARE (Yu et al., 2024), in addition to ensembling. TIES merges models by drop-elect-merge operations, where the "drop" step mitigates interference by removing redundant delta parameters based on their magnitudes.

5.2 Distillation Experiments
Table 4 and Table 5 present the performance and bias of both teacher models and student models after distillation. As observed, the student models effectively inherit the bias direction of their respective teacher models, while also achieving improved performance, except for the F1-score of LLaVA base model distilled from ChatGLM4. These results demonstrate that we can successfully prepare models for merging-based debiasing through distillation, without the need for elaborate training labels.

For the subsequent merging experiments, we apply our proposed distill-merge pipeline for debiasing when LLaVA serves as the student model. For InternVL as the student model, we further compare our proposed dynamic dropping method with baseline merging approaches, as InternVL itself exhibits a weak bias and can therefore be used as the base model.

5.3 Merging Experiments
This section analyzes the results on the testing set of MMSD2.0 (Qin et al., 2023).

For the LLaVA base model, we compare the performance of merged models against their original counterparts. As shown in Table 5, the average merging strategy fails to surpass the negative-biased model. However, applying DARE (fixed dropping) leads to significant improvements, with both accuracy and F1-score approximating those of the zero-shot inference of teacher models, alongside a substantial reduction in the absolute value of BR. This highlights the potential of our distill-merge pipeline for debiasing tasks when combined with a well-designed merging method.

Similarly, Table 4 implies that all merging strategies significantly reduce the absolute value of BR, compared to student base models distilled from biased models into InternVL, further demonstrating the effectiveness of our distill-merge pipeline. Moreover, 3DM, which introduces a tailored dropping mechanism in the "merge" phase, achieves state-of-the-art performance in accuracy, F1-score and BR across all merging approaches. This under-
<table>
  <tr>
    <th>Model</th>
    <th>Method</th>
    <th>Strategy</th>
    <th>Acc</th>
    <th>F1</th>
    <th>TP</th>
    <th>FP</th>
    <th>TN</th>
    <th>FN</th>
    <th>Bias direction</th>
    <th>Bias Ratio</th>
  </tr>
  <tr>
    <td>LLaVA-v1.5-7b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.516</td>
    <td>0.628</td>
    <td>982</td>
    <td>1110</td>
    <td>262</td>
    <td>55</td>
    <td>+</td>
    <td>0.765</td>
  </tr>
  <tr>
    <td>ChatGLM4-9b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.689</td>
    <td>0.555</td>
    <td>466</td>
    <td>177</td>
    <td>1195</td>
    <td>571</td>
    <td>-</td>
    <td>-0.422</td>
  </tr>
  <tr>
    <td>InternVL-2.5-8b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.499</td>
    <td>0.509</td>
    <td>625</td>
    <td>796</td>
    <td>576</td>
    <td>412</td>
    <td>weak</td>
    <td>0.183</td>
  </tr>
  <tr>
    <td rowspan="2">Distillation</td>
    <td>positive learning</td>
    <td></td>
    <td>0.543</td>
    <td>0.629</td>
    <td>934</td>
    <td>998</td>
    <td>374</td>
    <td>103</td>
    <td>+</td>
    <td>0.628</td>
  </tr>
  <tr>
    <td>negative learning</td>
    <td></td>
    <td>0.644</td>
    <td>0.428</td>
    <td>321</td>
    <td>141</td>
    <td>1231</td>
    <td>716</td>
    <td>-</td>
    <td>-0.588</td>
  </tr>
  <tr>
    <td rowspan="3">InternVL-2.5-8b</td>
    <td>average merging</td>
    <td></td>
    <td>0.688</td>
    <td>0.614</td>
    <td>599</td>
    <td>314</td>
    <td>1058</td>
    <td>438</td>
    <td>weak</td>
    <td>-0.194</td>
  </tr>
  <tr>
    <td>TIES</td>
    <td></td>
    <td>0.648</td>
    <td>0.484</td>
    <td>397</td>
    <td>208</td>
    <td>1164</td>
    <td>640</td>
    <td>-</td>
    <td>-0.466</td>
  </tr>
  <tr>
    <td>DARE</td>
    <td></td>
    <td>0.684</td>
    <td>0.609</td>
    <td>592</td>
    <td>316</td>
    <td>1056</td>
    <td>445</td>
    <td>weak</td>
    <td>-0.199</td>
  </tr>
  <tr>
    <td>3DM</td>
    <td></td>
    <td></td>
    <td>0.697</td>
    <td>0.643</td>
    <td>658</td>
    <td>351</td>
    <td>1021</td>
    <td>379</td>
    <td>weak</td>
    <td>-0.110</td>
  </tr>
  <tr>
    <td>Ensembling</td>
    <td>ensembling</td>
    <td></td>
    <td>0.663</td>
    <td>0.516</td>
    <td>431</td>
    <td>205</td>
    <td>1159</td>
    <td>605</td>
    <td>-</td>
    <td>-0.434</td>
  </tr>
</table>

Table 4: Results of applying multiple debiasing methods, including average merging, fixed dropping (DARE), our proposed 3DM and ensembling methods. "+" and "-" indicate that the model tends to give positive and negative answers.

<table>
  <tr>
    <th>Model</th>
    <th>Method</th>
    <th>Strategy</th>
    <th>Acc</th>
    <th>F1</th>
    <th>TP</th>
    <th>FP</th>
    <th>TN</th>
    <th>FN</th>
    <th>Bias direction</th>
    <th>Bias Ratio</th>
  </tr>
  <tr>
    <td rowspan="2">LLaVA-v1.5-7b</td>
    <td>Distillation</td>
    <td>positive learning</td>
    <td>0.516</td>
    <td>0.628</td>
    <td>982</td>
    <td>1110</td>
    <td>262</td>
    <td>55</td>
    <td>+</td>
    <td>0.765</td>
  </tr>
  <tr>
    <td>Distillation</td>
    <td>negative learning</td>
    <td>0.710</td>
    <td>0.666</td>
    <td>572</td>
    <td>233</td>
    <td>1139</td>
    <td>465</td>
    <td>-</td>
    <td>-0.279</td>
  </tr>
  <tr>
    <td rowspan="2">Merging</td>
    <td>Merging</td>
    <td>average merging</td>
    <td>0.671</td>
    <td>0.474</td>
    <td>357</td>
    <td>113</td>
    <td>1259</td>
    <td>680</td>
    <td>-</td>
    <td>-0.573</td>
  </tr>
  <tr>
    <td>Merging</td>
    <td>DARE</td>
    <td>0.714</td>
    <td>0.649</td>
    <td>617</td>
    <td>290</td>
    <td>1082</td>
    <td>400</td>
    <td>weak</td>
    <td>-0.189</td>
  </tr>
  <tr>
    <td>Ensembling</td>
    <td>ensembling</td>
    <td></td>
    <td>0.716</td>
    <td>0.693</td>
    <td>773</td>
    <td>421</td>
    <td>951</td>
    <td>264</td>
    <td>weak</td>
    <td>0.05</td>
  </tr>
</table>

Table 5: Results of applying debiasing methods on LLaVA-based models. Because LLaVA itself has a positive bias, we apply the original model to the "positive learning" row.

<table>
  <tr>
    <th>Model</th>
    <th>Ablation type</th>
    <th>Acc</th>
    <th>F1</th>
    <th>TP</th>
    <th>FP</th>
    <th>TN</th>
    <th>FN</th>
    <th>Bias direction</th>
    <th>Bias Ratio</th>
  </tr>
  <tr>
    <td rowspan="4">InternVL-2.5-8b</td>
    <td>Bias-free</td>
    <td>0.663</td>
    <td>0.661</td>
    <td>790</td>
    <td>564</td>
    <td>808</td>
    <td>247</td>
    <td>weak</td>
    <td>0.173</td>
  </tr>
  <tr>
    <td>Uni. & Bi.</td>
    <td>0.649</td>
    <td>0.477</td>
    <td>386</td>
    <td>195</td>
    <td>1177</td>
    <td>651</td>
    <td>-</td>
    <td>-0.486</td>
  </tr>
  <tr>
    <td>Async.</td>
    <td>0.691</td>
    <td>0.637</td>
    <td>654</td>
    <td>362</td>
    <td>1010</td>
    <td>383</td>
    <td>weak</td>
    <td>-0.105</td>
  </tr>
  <tr>
    <td>3DM</td>
    <td>0.697</td>
    <td>0.643</td>
    <td>658</td>
    <td>351</td>
    <td>1021</td>
    <td>379</td>
    <td>weak</td>
    <td>-0.110</td>
  </tr>
</table>

Table 6: Ablation study. In 3DM, we classify each position into two categories based on their signs. In this part, we remove one of them, and test the method’s performance. We also examine the performance of the synchronized dropping mechanism.

scores the effectiveness and superiority of dynamic dropping.

We provide insights into why 3DM outperforms other merging approaches. While TIES mitigates interference between delta parameters through sign selection, it struggles in cases like Fig. 3(c), where it may remain on the wrong side. DARE, on the other hand, applies a uniform drop rate to all delta parameters, disregarding their distinct roles. However, as illustrated in Fig. 4, the proportion of bias-free delta parameters (blue) is comparable to that of unidirectional and bidirectional delta parameters (red), highlighting the necessity of dynamically assigning drop rates based on their roles (Sec. 4.2) and merging conditions (Fig. 3).

5.4 Ablation Study

To better understand the role of dynamic dropping in 3DM, we conduct an ablation study by modifying key components of the mechanism.

As shown in Table 6, Bias-free, which replaces the adaptive drop rates of unidirectional and bidirectional deltas in Eq. 5 with a fixed rate, results in lower accuracy, along with a higher absolute value of BR. This suggests that a fixed drop rate fails to effectively leverage the variations in \( d_{ij}^P \) and \( d_{ij}^N \). Similarly, Uni. & Bi., which follows DARE by applying a fixed drop rate to bias-free deltas instead of fully preserving them, also performs suboptimally compared to 3DM.

Additionally, we evaluate a less aggressive strategy than 3DM (synchronized dropping), called Async., which drops delta parameters individually based on Eq. 5. This reduces the likelihood of simultaneously eliminating delta parameters\(^1\) in the scenario shown in Fig. 3(c). While this approach

\(^1\)The final parameter at that position defaults to the base model’s.
<table>
  <tr>
    <th>Model</th>
    <th>Method</th>
    <th>Strategy</th>
    <th>Acc</th>
    <th>F1</th>
    <th>TP</th>
    <th>FP</th>
    <th>TN</th>
    <th>FN</th>
    <th>Bias direction</th>
    <th>Bias Ratio</th>
  </tr>
  <tr>
    <td>LLaVA-v1.5-7b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.445</td>
    <td>0.587</td>
    <td>952</td>
    <td>1331</td>
    <td>119</td>
    <td>7</td>
    <td>+</td>
    <td>0.911</td>
  </tr>
  <tr>
    <td>ChatGLM4-9b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.713</td>
    <td>0.587</td>
    <td>492</td>
    <td>225</td>
    <td>1225</td>
    <td>467</td>
    <td>-</td>
    <td>-0.332</td>
  </tr>
  <tr>
    <td>InternVL-2.5-8b</td>
    <td>/</td>
    <td>zero-shot inference</td>
    <td>0.483</td>
    <td>0.473</td>
    <td>559</td>
    <td>846</td>
    <td>604</td>
    <td>400</td>
    <td>weak</td>
    <td>0.166</td>
  </tr>
  <tr>
    <td colspan="2">Distillation</td>
    <td>positive learning</td>
    <td>0.501</td>
    <td>0.592</td>
    <td>871</td>
    <td>1113</td>
    <td>337</td>
    <td>88</td>
    <td>+</td>
    <td>0.676</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>negative learning</td>
    <td>0.667</td>
    <td>0.466</td>
    <td>350</td>
    <td>193</td>
    <td>1257</td>
    <td>609</td>
    <td>-</td>
    <td>-0.502</td>
  </tr>
  <tr>
    <td rowspan="4">InternVL-2.5-8b</td>
    <td colspan="2">average merging</td>
    <td>0.691</td>
    <td>0.619</td>
    <td>605</td>
    <td>390</td>
    <td>1060</td>
    <td>354</td>
    <td>weak</td>
    <td>-0.100</td>
  </tr>
  <tr>
    <td colspan="2">TIES</td>
    <td>0.676</td>
    <td>0.519</td>
    <td>422</td>
    <td>244</td>
    <td>1206</td>
    <td>537</td>
    <td>-</td>
    <td>-0.392</td>
  </tr>
  <tr>
    <td colspan="2">DARE</td>
    <td>0.686</td>
    <td>0.613</td>
    <td>600</td>
    <td>397</td>
    <td>1053</td>
    <td>359</td>
    <td>weak</td>
    <td>-0.101</td>
  </tr>
  <tr>
    <td colspan="2">3DM</td>
    <td>0.691</td>
    <td>0.636</td>
    <td>651</td>
    <td>436</td>
    <td>1014</td>
    <td>308</td>
    <td>weak</td>
    <td>-0.020</td>
  </tr>
  <tr>
    <td colspan="2">Ensembling</td>
    <td>ensembling</td>
    <td>0.680</td>
    <td>0.530</td>
    <td>433</td>
    <td>241</td>
    <td>1200</td>
    <td>526</td>
    <td>-</td>
    <td>-0.381</td>
  </tr>
</table>

Table 7: Performance of methods on MMSD1.0 dataset.

achieves a slightly lower BR, it suffers a small drop in accuracy and F1-score. This could be due to it tends to retain a delta parameter in a single wrong direction, thus degenerating into TIES. This reinforces the effectiveness of the synchronized dropping mechanism, which not only preserves flexibility in handling unidirectional deltas but also forces the dropping of delta parameters in the bidirectional delta condition, where they may introduce greater bias or interference.

5.5 Comparison with Ensemble

We conduct a systematic comparison between our 3DM method and ensemble approaches. For sarcasm detection, ensemble methods generate individual probability distributions and aggregate them for final predictions. While achieving acceptable performance, these methods incur substantial computational overhead, with inference costs scaling as \( O(n) \), compared to \( O(1) \) for merging methods. This establishes a fundamental advantage for merging approaches.

In our experiments, we implement basic averaging ensemble, where model distributions are arithmetically averaged. As shown in Table 4, Table 5, and Table 7, this approach demonstrates limited effectiveness on the testing dataset. Although more sophisticated ensemble techniques might surpass 3DM’s performance, they cannot overcome the inherent computational limitations of all ensemble methods, which remain a fundamental constraint compared to merging approaches.

5.6 Generalizability Analysis

In order to test the generalizability of our method, we validate our method on the testing set of MMSD1.0 (Cai et al., 2019). We retain the checkpoints in Sec. 5.2, and apply average merging, TIES, DARE and 3DM in exactly the same way as Sec. 5.3, but on the MMSD1.0 dataset. Table 7 presents the results of multiple methods, where 3DM exhibits the highest accuracy, the highest F1-score, and the lowest absolute value of BR. Moreover, all merging-based methods reduce the absolute value of BR. The results in Table 7 imply comparable tendency with Table 4, demonstrating the advanced generalizability of 3DM.

5.7 Hyperparameter Tuning for DARE

We search the hyperparameter on the validation set of MMSD2.0, and report the result in Table 8. Based on the result, we select 0.7 as the drop rate for DARE in our experiment.

6 Conclusion

In this study, we present a comprehensive analysis of biases in MLLMs, empirically demonstrating that the majority of existing MLLMs exhibit significant biases in sarcasm detection tasks, with varying directional tendencies. Our work represents the first systematic effort to develop an architecture-agnostic merging framework specifically designed to address and mitigate biases in models with divergent bias orientations, particularly in debiasing tasks.

The core contributions of our research include: (1) a generalized distill-merge pipeline applicable to both black-box and white-box MLLMs, and (2) a novel dynamic dropping mechanism that assigns individualized drop rates to delta parameters based on each parameter’s functional role in the model. Notably, our distill-merge pipeline serves as a general, plug-and-play component that can be seamlessly integrated into various merging methodologies.

This research establishes a new paradigm for bias mitigation in MLLMs through advanced merg-
<table>
  <tr>
    <th>Method</th>
    <th>Hyperparameter</th>
    <th>Acc</th>
    <th>F1</th>
    <th>TP</th>
    <th>FP</th>
    <th>TN</th>
    <th>FN</th>
    <th>Bias direction</th>
    <th>Bias Ratio</th>
  </tr>
  <tr>
    <td rowspan="4">DARE</td>
    <td>drop rate = 0.1</td>
    <td>0.676</td>
    <td>0.592</td>
    <td>566</td>
    <td>304</td>
    <td>1064</td>
    <td>476</td>
    <td>weak</td>
    <td>-0.235</td>
  </tr>
  <tr>
    <td>drop rate = 0.3</td>
    <td>0.682</td>
    <td>0.588</td>
    <td>547</td>
    <td>272</td>
    <td>1096</td>
    <td>495</td>
    <td>weak</td>
    <td>-0.276</td>
  </tr>
  <tr>
    <td>drop rate = 0.5</td>
    <td>0.686</td>
    <td>0.610</td>
    <td>591</td>
    <td>306</td>
    <td>1062</td>
    <td>451</td>
    <td>weak</td>
    <td>-0.209</td>
  </tr>
  <tr>
    <td>drop rate = 0.7</td>
    <td>0.694</td>
    <td>0.613</td>
    <td>584</td>
    <td>279</td>
    <td>1089</td>
    <td>458</td>
    <td>weak</td>
    <td>-0.236</td>
  </tr>
</table>

Table 8: Hyperparameter sensitivity of DARE on the validation set of MMSD2.0

ing techniques, while simultaneously introducing a parameter-specific analytical framework for understanding and utilizing delta parameters. We anticipate that our findings will stimulate further research in this emerging area of MLLM optimization and bias reduction.

Limitations

In this study, we introduce a distill-merge pipeline designed for architectural alignment, alongside a dynamic merging mechanism that assigns a unique drop rate for each delta parameter. Nonetheless, the current implementation of assigning drop rates overlooks the intricate interplay of synergistic and antagonistic interactions among multiple delta parameters, which could potentially influence the optimization process and outcomes. For instance, several delta parameters altogether contributes to biases, while any one of them individually can not. This limitation suggests a fertile ground for future research to explore and integrate these complex parameter interactions, thereby refining the mechanism’s efficacy and robustness.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (62076008) and the Key Project of Natural Science Foundation of China (61936012).

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