# M2K-VDG: Model-Adaptive Multimodal Knowledge Anchor Enhanced Video-grounded
Dialogue Generation

Hongcheng Liu 1, Pingjie Wang${}^{1},2$, Yu Wang∗1,2, Yanfeng Wang1,2
1Cooperative Medianet Innovation Center, Shanghai Jiao Tong University
2Shanghai Artificial Intelligence Laboratory
<EMAIL_ADDRESS>

###### Abstract

Video-grounded dialogue generation (VDG) requires the system to generate a
fluent and accurate answer based on multimodal knowledge. However, the
difficulty in multimodal knowledge utilization brings serious hallucinations
to VDG models in practice. Although previous works mitigate the hallucination
in a variety of ways, they hardly take notice of the importance of the
multimodal knowledge anchor answer tokens. In this paper, we reveal via
perplexity that different VDG models experience varying hallucinations and
exhibit diverse anchor tokens. Based on this observation, we propose M2K-VDG,
a model-adaptive multimodal knowledge anchor enhancement framework for
hallucination reduction. Furthermore, we introduce the counterfactual effect
for more accurate anchor token detection. The experimental results on three
popular benchmarks exhibit the superiority of our approach over state-of-the-
art methods, demonstrating its effectiveness in reducing hallucinations.

##  1 Introduction

The task of video-grounded dialogue generation (VDG) involves generating
responses to queries by leveraging multimodal knowledge sources AlAmri et al.
(2019), including visual content, auditory features, and previous dialogue
exchanges, as illustrated in Figure 1. This task stands out from traditional
knowledge-based question-answering challenges due to the intricate nature of
its knowledge sources. The integration of diverse modalities introduces a
level of complexity not typically encountered in other domains, making
effective utilization of this information a formidable challenge Liu et al.
(2023). Consequently, VDG systems are more prone to generating inaccurate or
irrelevant responses, which is a phenomenon known as ‘hallucination’ in the
realm of large language models. To reduce hallucination and enhance
performance, there are various methods proposed to address this issue, such as
external video description and various dataset enhancement Shah et al. (2022);
Le et al. (2023). Besides, Cherian et al. (2022) and Geng et al. (2021)
utilize symbolic graphs for multimodal alignment. However, these approaches
only focus on more inputs but ignore reducing the hallucination specifically
based on its characteristics.

Figure 1: The demonstration of the video-grounded dialogue generation task,
where the system is required to generate a fluent and accurate response
according to the multimodal knowledge. However, the difficulty in multimodal
knowledge utilization always leads to the experience of the hallucination.

To analyze the hallucination in the VDG, we evaluated it across different
models using perplexity. This measurement compares the generated probability
and the ground answer, and is commonly used to assess the quality of generated
results Chen and Deng (2023). As shown in Figure 2, the hallucination varies
among tokens within a sentence, with certain tokens being more susceptible to
hallucination. In this paper, we refer to these tokens as ‘anchor tokens’,
representing key tokens in reducing hallucination. Furthermore, these anchor
tokens are closely linked to multimodal knowledge, hence, we categorize them
as ‘multimodal knowledge anchor tokens’. It can be seen in Figure 2 that the
degree of perplexity notably varies across models, and different models tend
to hallucinate more on different multimodal knowledge anchor tokens. Given the
significant degree of hallucination caused by anchor tokens, it is important
to use these tokens effectively to reduce hallucinations.

Figure 2: The perplexity of answer tokens derived by different VDG models
trained with the same multimodal content, and the higher perplexity means the
more serious hallucination. The question is ‘tell me the sequence in which the
event occurred?‘, and the label in red denotes the knowledge-related tokens of
ground answers through human detection. It is noted that different models
experience hallucinations in various anchor tokens, which reflects the various
multimodal knowledge anchors among different models.

Considering the importance of the anchor tokens, FocusL Deng et al. (2023)
forces the model to be more attentive to knowledge anchor tokens and makes
great progress in hallucination mitigation. Nevertheless, this method employs
cosine similarity between the answer tokens and given text knowledge to select
anchor tokens, regarding the anchor tokens as the immutable standards among
various models and overlooking the varying knowledge anchor tokens of
different methods as illustrated in Figure 2. Moreover, due to semantic gaps,
this method cannot be applied to video-grounded dialogue generation directly.

To address the aforementioned issues, we propose M2K-VDG, a Model-adaptive
Multimodal Knowledge anchor enhancement framework for the VDG task. This
framework reduces model hallucinations by enhancing multimodal knowledge
anchor tokens from the model itself. Based on the observation in Figure 2, we
split our M2K-VDG approach into two stages: 1) Anchor Token Detection, and 2)
Model Hallucination Reduction. In the first stage, we utilize the perplexity
as the anchor weight of each answer token derived by well-trained models for
model adaptive anchor detection. In the second stage, the derived anchor
weight is then normalized and merged into the original Softmax function to
enhance training for knowledge anchors. To further enhance the precision and
resilience of our anchor detection process, we introduce an innovative method
based on counterfactual effects. This method provides a more nuanced
assessment of anchor significance by examining the hypothetical impact of
altering the inputs. Through this approach, we aim to refine the detection and
utilization of knowledge anchors within the M2K-VDG framework, ensuring a more
reliable and effective reduction in model hallucinations.

To evaluate the performance of M2K-VDG, we compare it with a range of state-
of-the-art methods on three popular VDG benchmarks: AVSD10 dataset, NExT-OE
dataset, and MUSIC-AVQA dataset. Experimental results verify that M2K-VDG
exhibits significantly superior performance compared to established baseline
methodologies across all benchmarks, demonstrating the superiority and
effectiveness of our proposed multimodal knowledge anchor enhancement
approach.

Our main contributions are three folds:

  1. $\bullet$

Diverse Hallucination Patterns Across VDG Models: Our research uncovers that
various VDG models exhibit different patterns of hallucinations. To address
this issue, we introduce M2K-VDG, a novel framework specifically designed to
adaptively reduce hallucinations. This is achieved through two core
strategies: Detection of Anchor Tokens and Reduction of Model Hallucinations,
which together aim to improve model accuracy and reliability by anchoring
generated content to detectable knowledge points within the model’s output.

  2. $\bullet$

Enhanced Anchor Detection with Counterfactual Analysis: We advance the
robustness of our framework’s anchor detection mechanism by incorporating a
counterfactual effect. This method significantly improves our ability to
detect multimodal knowledge anchor tokens, offering a more effective means of
identifying critical information points that ground the model’s output in
reality.

  3. $\bullet$

Comprehensive Evaluation on Popular VDG Benchmarks: We rigorously test M2K-VDG
against three widely recognized VDG benchmarks to validate its effectiveness
and efficiency. The results clearly demonstrate that M2K-VDG surpasses
existing state-of-the-art methods, showcasing its superiority in reducing
model hallucinations and enhancing the overall quality of visual dialog
generation. This underscores the potential of M2K-VDG to set a new benchmark
in the field, offering a robust solution to the longstanding challenge of
model hallucination in VDG tasks.

##  2 Related Works

Video-grounded dialogue generation (VDG) requires the system to generate
answers related to the questions based on multimodal information Schwartz et
al. (2019), which consists of video, audio, and dialogue history. Compared
with other visual-language tasks, the VDG exhibits significant difficulty in
the utilization of multimodal knowledge consisting of the visual, auditory,
and textual modalities Pasunuru et al. (2018); Liu et al. (2023), which leads
to serious hallucinations.

To more effectively reduce hallucination, a variety of methods have been
proposed that better utilize different modalities. Several models minimize
hallucination by enhancing the connection between visual features and
questions. This is achieved through various feature extractors Li et al.
(2021); Yamazaki et al. (2022), external visual information Le et al. (2020a);
Jin et al. (2023), and fusion strategies Zhao et al. (2022); Yoonseok et al.
(2022). In addition, manifold methods are proposed for enhancing the
utilization of auditory information Lao et al. (2023); Yoon et al. (2023); Li
et al. (2022). Furthermore, there are many improvement strategies proposed for
the textual modality, such as DialogMCF Chen et al. (2023) and SCGA Kim et al.
(2021). However, all the above approaches are short of analyzing the inherent
characteristics of the hallucination, thereby attaining limited performance
improvement. In this paper, we propose M2K-VDG to tackle this problem.
Specifically, M2K-VDG detects the anchor tokens with more serious
hallucinations and utilizes them to achieve higher answering quality.

##  3 Preliminaries

In this section, we introduce the basic concept of causal inference and
counterfactual effect. For consistency, we use uppercase letters (e.g. $X$) to
represent the variable and lowercase letters (e.g. $x$) for observed values.

(a) $Y_{x,M_{x}}$

(b) $Y_{x,M_{x^{*}}}$

(c) $Y_{x,M_{x^{*}}}$

(d) $Y_{x^{*},M_{x^{*}}}$

Figure 3: The illustration of the causal effect.

###  3.1 Causal Graph

The causal connections among distinct variables are encapsulated in a causal
graph $\mathcal{G}=\\{\nu,\varepsilon\\}$, where $\nu$ signifies the set of
variables, and $\varepsilon$ characterizes the causal relationships existing
between them. The example in Figure 3 illustrates the causality between
variable $X$, mediator $M$, and variable $Y$.The $X\to Y$ denotes variable $X$
has a direct causal effect on variable $Y$, and the $X\to M\to Y$ denotes
variable $X$ has an indirect causal effect on variable $Y$ via mediator $M$.

###  3.2 Counterfactual notations

For computational reasoning, the causal graph can be formulated as causal
notations, which is

$Y_{x,m}=Y(do(X=x),M=m)=Y(X=x,M=m),$ (1)

where $m=M_{x}=M(X=x)$, the $do$ operator can be omitted without any
confounder in $X$. Furthermore, the variable $X$ can be set as $x^{*}$ to
represent the negation operation in the counterfactual reasoning, which is

$Y_{x^{*},m}=Y(X=x^{*},M=m),$ (2)

where the negation operation can be achieved by omitting input $X$ in
practice.

Figure 4: The overview of the M2K-VDG: (a) Anchor Token Detection aims to
detect the multimodal knowledge anchor tokens in the grounded answer through
well-trained models, and is categorized into perplexity- and counterfactual
effect-based. (b) Model Hallucination Reduction normalizes the anchor weight
and focuses on the new model training via temperature change derived by the
normalized weight.

###  3.3 Counterfactual Effect

Counterfactual effects are often used to measure the impact of the input
variable on the output variable, which can minimize other distractions. In
practice, it often uses total effect (TE) to measure the impact through the
difference in $Y$ in the condition of the different treatments on $X$, which
is denoted as:

$TE=Y_{x,M_{x}}-Y_{x^{*},M_{x^{*}}},$ (3)

where the TE can reflect the total impact of $X$ on $Y$. Additionally, the
total effect can be further divided into $TIE$ and $NDE$, which can measure
the impact on variables in a more fine-grained way. The formulations are

$TIE=Y_{x,M_{x}}-Y_{x,M_{x^{*}}},$ (4)
$NDE=Y_{x,M_{x^{*}}}-Y_{x^{*},M_{x^{*}}},$ (5)

where the TIE can reflect the impact of $M$ on $Y$ and the NDE can reflect the
effect of $X$ on $Y$ on condition of $M_{x^{*}}$. Considering it can reflect
the impact of $X$ on $Y$ is highly related to the correlation between the $X$
and $Y$ without any other factors interference, the counterfactual effect is
widely used for impact measurement and relevance calculation. Therefore, we
utilize the counterfactual effect as the metric to measure the anchor degree
of answer tokens with multimodal knowledge for the VDG task.

##  4 Methods

###  4.1 Task Formulation

Given the video $V$, audio $A$, dialogue history $H$, and question $Q$, the
video-grounded dialogue generation aims to generate appreciative and fluent
responses $Y=\\{y_{1},y_{2},\dots,y_{T}\\}$, which contains $T$ tokens. For
conciseness, we use multimodal knowledge $K$ to represent the set of the
video, audio, and dialogue history, that is, $K=\\{V,A,H\\}$. This task can be
formulated as

$P(Y|K,Q)=\prod_{t=1}^{T}P(y_{t}|K,Q,Y_{<t}).$ (6)

###  4.2 Overview

The overview of the M2K-VDG is shown in Figure 4, which is divided into two
stages: 1) anchor token detection, and 2) model hallucination reduction.
Specifically, for the first stage, we measure the perplexity of the answer
tokens by well-trained models and derive the anchor degree of each token. The
second stage aims to utilize the derived anchor degrees of tokens as a weight
to enhance the untrained model learning process, in which the anchor degree is
normalized and merged into the answer probability distribution and loss
function.

We adopt the BART model as our backbone and extend it to adapt to multi-
modality input, named VideoBART. Specifically, we use the BART encoder as the
multimodal knowledge extractor and the BART decoder as the response generator,
as the encoder and decoder are suitable to be responsible for complex
understanding and detailed reasoning. In detail, we feed the video, audio, and
dialogue history into the BART encoder to obtain a multimodal representation,
and then feed it into the BART decoder along with the question to generate the
response. Specifically, we use special tokens to separate different
modalities.

###  4.3 Anchor Token Detection

As we have observed in Figure 2, VDG models are prone to experience
hallucination for specific anchor tokens, which are correlated to multimodal
knowledge and more easily to be falsely predicted. Furthermore, the anchor
tokens have limited opportunity to be optimized as they are highly correlated
to specific samples. To address the above issues, we first calculate the
anchor degree of each answer token and then emphasize the tokens with higher
anchor weights during training.

However, considering the huge gap in representations between natural language
and other modalities, the traditional strategies for relevance calculation
(e.g., cosine similarity) are unsuitable for the VDG task. To tackle this
problem, we first leverage perplexity to detect the anchor degree of tokens
for its high correlation with the hallucination probability. Furthermore, we
improve the detection method by the counterfactual effect, which is more
effective and robust. It is noted that both strategies can adapt to and
enhance various backbones, which we will demonstrate in Section 5.5.

####  4.3.1 Perplexity-based Detection

Since the perplexity can efficiently identify anchor tokens from the model, as
demonstrated in Figure 2, we use it to detect these tokens and determine the
anchor weight for training. The anchor weight $W_{P}$ derived by this
perplexity-based detection method is formulated as

$W_{P}=-\log P\left(y_{t}\mid K,Q,Y_{<t}\right).$ (7)

It is noted that the anchor weight is obtained by a well-trained model, which
can be any existing VDG model. While the perplexity-based detection method can
be effective, it is easily influenced by variations in noise during the
training phase, thereby resulting in different anchors. Moreover, this method
is unable to specifically detect multimodal knowledge anchor tokens. It
instead represents the cumulative effect on different modality inputs, which
imposes limitations in practice.

####  4.3.2 Counterfactual Effect-based Detection

To tackle these problems, we further introduce the counterfactual effect to
enhance the robustness and effectiveness of anchor degree estimation.
Specifically, as described in Section 3.3, we utilize two well-trained models
for response generation under different conditions, one of which is trained
given both multimodal knowledge and the question, and the other is only fed
with the question. On this occasion, the counterfactual effect is denoted as

$TIE=Y(K=k,Q=q)-Y(K=k^{*},Q=q),$ (8)

We reformulate this effect in a perplexity form for the VDG task as

$W_{CF}=\left|\log P_{1}\left(y_{t}\mid Q,Y_{<t}\right)-\log
P_{2}\left(y_{t}\mid K,Q,Y_{<t}\right)\right|,$ (9)

where $\left|\cdot\right|$ denotes the absolute value, $P_{1}$ and $P_{2}$
represent the perplexity distributions of the two models trained with
different inputs. In this way, we avoid the estimation noise in the training
stage. Additionally, this method can also be extended to measure the anchor
degree of tokens for a specific modality by controlling the input modality to
$P_{1}$.

Methods | BLEU1 | BLEU2 | BLEU3 | BLEU4 | METEOR | ROUGH-L | CIDEr
---|---|---|---|---|---|---|---
AV-trans (2022) | - | - | - | 0.247 | 0.191 | 0.437 | 0.566
Ext.AV* (2022) | - | - | - | 0.371 | 0.245 | 0.535 | 0.869
NLM (2022) | 0.641 | 0.489 | 0.379 | 0.298 | 0.225 | 0.502 | 0.804
MED-CAT (2022) | 0.670 | 0.541 | 0.441 | 0.365 | 0.241 | 0.526 | 0.906
UniVL-obj* (2022) | 0.673 | 0.545 | 0.448 | 0.372 | 0.243 | 0.530 | 0.912
TSF(ensemble) (2022) | 0.680 | 0.558 | 0.461 | 0.385 | 0.247 | 0.539 | 0.957
DialogMCF (2023) | 0.693 | 0.556 | 0.450 | 0.369 | 0.249 | 0.536 | 0.912
MSG-BART* (2023) | - | - | - | 0.390 | 0.268 | 0.556 | 1.008
M2K-VDG (Ours) | 0.723 | 0.589 | 0.483 | 0.398 | 0.271 | 0.560 | 1.020
Table 1: Evaluation results of our model compared with baseline approaches on
AVSD10 dataset. The * denotes the result obtained with the external knowledge,
including the detected objects, scene graphs, and video descriptions.

###  4.4 Model Hallucination Reduction

As we have obtained the anchor weight for each answer token, we apply
normalization and merge it into the original all-1 vector to obtain the final
anchor weight of tokens. Then, we utilize the final weight in the Softmax
function to reduce the multimodal knowledge hallucination by forcing the model
to be more attentive to anchor tokens.

####  4.4.1 Weight Normalization

Although these two detection strategies can both measure the anchor degree of
the answer token effectively, they cannot be applied for hallucination
reduction directly due to their unequal ranges. Therefore, we normalize the
obtained weight and merge it into the original all-1 vector, which is
formulated as

$W_{final}=\\{w_{t}|w_{t}=1+\frac{w_{t}}{\sum_{n=0}^{T}w_{n}},w_{t}\in W\\},$
(10)

where $W$ denotes the $W_{P}$ or $W_{CF}$, and $T$ represents the total number
of tokens within a sentence.

####  4.4.2 Anchor Enhanced Loss

After obtaining the final weight, we adopt it as the temperature rate in the
Softmax function to adjust the sharpness of each answer token, which is
formulated as

$P_{SW}(y_{t}|K,Q,Y_{<t})=\rm{Softmax}(W_{final}*L_{g}),$ (11)

where $L_{g}$ denotes the output logits distribution. The modified probability
$P_{SW}$ is then utilized to enhance the loss function as

$\mathcal{L_{SW}}=-\sum_{t=1}^{T}\log P_{SW}\left(y_{t}\mid
K,Q,Y_{<t}\right).$ (12)

##  5 Experiments

###  5.1 Datasets

Experiments are conducted on three popular VDG benchmarks, which contain
AVSD10, NExT-OE, and MUSIC-AVQA datasets. These datasets are different in
scenes, modalities, and fields. Furthermore, we adopt different evaluation
metrics from various aspects, including fluent expression (BLEU1-4, METEOR,
ROUGH-L, CIDEr), semantic representation (WUPS Score111Wu-Palmer Similarity
Score: evaluate the semantic similarity between the generated answers and the
ground truth answers, which are available in the dataset.), and accurate
generation (Accuracy). The details of the datasets are given in Appendix A.

Methods | WUPS
---|---
HCRN (2020b) | 23.92
HME (2019) | 24.06
UATT (2017) | 24.25
HGA (2020) | 25.18
ClipBERT (2021) | 24.17
MSG-BART* (2023) | 27.45
KcGA* (2023) | 28.20
M2K-VDG(Ours) | 29.16
Table 2: The WUPS scores of our model compared with baseline approaches on the NExT-OE dataset. The * denotes the result obtained with external knowledge, including the knowledge base and sence graph. Methods | $A_{C}$ | $A_{Cm}$ | $V_{C}$ | $V_{L}$ | $AV_{E}$ | $AV_{C}$ | $AV_{L}$ | $AV_{Cm}$ | $AV_{T}$ | Average
---|---|---|---|---|---|---|---|---|---|---
CONV${\dagger}$ (2020) | 74.07 | 68.89 | 67.47 | 54.56 | 82.91 | 50.81 | 63.03 | 60.27 | 51.58 | 63.65
MCAN (2019) | 77.50 | 55.24 | 71.56 | 70.93 | 80.40 | 54.48 | 64.91 | 57.22 | 47.57 | 65.49
HCRN (2020b) | 68.59 | 50.92 | 64.39 | 61.81 | 54.47 | 41.53 | 53.38 | 52.11 | 47.69 | 55.73
HME (2019) | 74.76 | 63.56 | 67.97 | 69.46 | 80.30 | 53.18 | 63.19 | 62.69 | 59.83 | 66.45
AVSD (2019) | 72.41 | 61.90 | 67.39 | 74.19 | 81.61 | 58.79 | 63.89 | 61.52 | 61.41 | 67.44
P-AVQA${\dagger}$ (2021) | 74.36 | 64.56 | 69.39 | 75.65 | 81.21 | 59.33 | 64.91 | 64.22 | 63.23 | 68.93
AVST (2022) | 78.18 | 67.05 | 71.56 | 76.38 | 81.81 | 64.51 | 70.80 | 66.01 | 63.23 | 71.52
COCA (2023) | 79.94 | 67.68 | 75.10 | 75.43 | 83.50 | 66.63 | 69.72 | 64.12 | 65.57 | 72.33
M2K-VDG(Ours) | 82.39 | 65.15 | 78.11 | 78.12 | 82.49 | 70.75 | 64.13 | 62.49 | 66.54 | 72.87
Table 3: The accuracy of our model compared with baseline approaches on the MUSIC-AVQA dataset. (A: audio, V: visual, AV: audio-visual, C: Counting, Cm: Comparative, L: Location, E: Existential, T: Temporal). The ${\dagger}$ denotes the abbreviation of the method for convenient display. (CONV: CONVLSTM, P-AVQA: Pano-AVQA) Methods | BLEU4 | CIDEr
---|---|---
VideoBART | 0.384 | 0.987
$W_{P}$ | 0.388 | 1.002
$W_{CF}$ | 0.398 | 1.020
Table 4: Evaluation results of detection methods ablation experiments on AVSD10 dataset.  Methods | BLEU1 | BLEU2 | BLEU3 | BLEU4 | METEOR | ROUGH_L | CIDEr
---|---|---|---|---|---|---|---
AV-trans | - | - | - | 0.247 | 0.191 | 0.437 | 0.566
AV-trans+M2K | 0.606 | 0.469 | 0.354 | 0.276 | 0.201 | 0.466 | 0.629
NLM | 0.641 | 0.489 | 0.379 | 0.298 | 0.225 | 0.502 | 0.804
NLM+M2K | 0.699 | 0.563 | 0.458 | 0.376 | 0.255 | 0.543 | 0.964
TSF(ensemble) | 0.680 | 0.558 | 0.461 | 0.385 | 0.247 | 0.539 | 0.957
TSF+M2K | 0.675 | 0.555 | 0.462 | 0.389 | 0.246 | 0.537 | 0.957
VideoGPT* | 0.682 | 0.559 | 0.458 | 0.380 | 0.245 | 0.537 | 0.937
VideoGPT+M2K | 0.700 | 0.566 | 0.461 | 0.380 | 0.255 | 0.543 | 0.970
VideoBART | 0.707 | 0.571 | 0.466 | 0.384 | 0.262 | 0.545 | 0.987
VideoBART+M2K | 0.723 | 0.589 | 0.483 | 0.398 | 0.271 | 0.560 | 1.020
Table 5: Evaluation results of baseline ablation experiments on AVSD10
dataset. The * denotes the results we reproduced for the result missing and
the M2K is an abbreviation of the multimodal knowledge anchor enhancement.

###  5.2 Experimental Settings

We extract visual features by ActionCLIP Wang et al. (2021) and obtain
auditory features by Wav2CLIP Wu et al. (2022) with a time duration of 4s. In
the training phase, we choose $W_{CF}$ as the final anchor token detection
method. We initialize our model by BART-base
222https://huggingface.co/facebook/bart-base and adopt an AdamW optimizer with
a learning rate of 8.75e-5. The batch size is 32 and the smooth rate in
Equation 9 is 0.1. During the inference phase, we used the beam search
algorithm with a beam size of 6 and a penalty factor of 0.6.

###  5.3 Baselines

We use the following methods as our baseline system. (i) VideoGPT Li et al.
(2021) integrates visual-audio features and the dialogue text as a combined
multimodal input and feeds it into the GPT-2 to generate a response.
Furthermore, this is the champion in the AVSD8 challenge and we reproduce it
by official code for comparison. (ii) TSF Yamazaki et al. (2022) employs
TimeSformer as the visual feature extractor and enhances performance through
ensemble methods. (iii) AV-trans Shah et al. (2022) uses the encoder-decoder
structure as the backbone without pre-training and introduces bimodal
attention to fuse audio-visual features. (iv) NLM Huang et al. (2022)
identifies actions as video features, transmitting them to GPT-2 for
generating responses based on text.

###  5.4 Main Result

The main experimental results are shown in Table 1-3. It can be seen that our
M2K-VDG methods outperform other baselines across almost all metrics,
indicating their effectiveness in reducing hallucination. As shown in Table 1,
M2K-VDG performs significantly better than other methods, particularly on
BLEU4, ROUGH-L, and CIDEr metrics. This implies strong fluency and keyword-
generation capabilities. Notably, our method also surpasses MSG-BART, which
uses an external scene graph, especially on the CIDEr metric. This shows our
methods are capable of generating more significant answers without external
input. Furthermore, the WUPS score in Table 2 surpasses others by a huge
margin, which illustrates that our method can improve the capability in
semantic representation effectively. Also, the results in Table 2 surpass
others by a large scale, which illustrates that our method generates more
appropriate answers rather than hallucinations.

Furthermore, we compare the detailed capability of different types of
questions with the state-of-art model in the MUSIC-AVQA dataset, which is
shown in Table 3. It is important to note that our method outperforms others
in almost all question fields, which indicates that our method is effective in
hallucination reduction overall. Specifically, the performance in counting
questions is particularly outstanding, which may be because the counting
question is more related to the video content than others and has more deeper
anchor degree in number tokens. Nonetheless, the performance in addressing
audio-visual questions, particularly those about location and comparison,
falls short of expectations. This underscores the necessity for our method to
conscientiously incorporate modality fusion to enhance these specific aspects
in the future.

Figure 5: The anchor degree of token derived by two detection methods. The
question is ‘is it the last you see of him when he walks out of frame ?’ and
the red label denotes the knowledge-related tokens through human detection. We
observe that the perplexity-based technique locates ‘him’, ‘toward’, and
’fridge’ as the anchor tokens, which should be a set of ‘yes’ and ‘fridge’. By
contrast, the counterfactual effect-based method can detect them accurately,
which demonstrates its effectiveness and robustness.

###  5.5 Ablation Study

####  5.5.1 Detection Methods

To illustrate the effectiveness of the counterfactual effect, we adopt both
perplexity- and counterfactual effect-based anchor degree detection methods as
described in Section 4.3, and the results are shown in Table 4 and Figure 5.
As Table 4 shows, both two detection methods can improve the performance and
reduce the hallucination across all the metrics, especially on the CIDEr.
However, it is noted that the counterfactual effect-based detection strategy
enhances the model performance more effectively than the perplexity-based one,
which verifies the capability of the counterfactual effect in anchor token
detection and hallucination reduction. Additionally, Figure 5 also verifies
that the counterfactual effect enhances the anchor token detection.

####  5.5.2 Baselines Adaptiveness

To validate the robustness of our method, we extend it to various baseline
models and evaluate the performance on the AVSD10 official test set, shown in
Table 5. For better validation of the robustness, we conduct experiments on
five baseline models that differ in feature extractors, architectures, and
modality fusion strategies, which is described in Section 5.3. As shown in
Table 5, all the baselines show an improvement in all metrics, especially the
TSF+M2K can get comparable performance with the ensemble method. Among the
metrics, the performance in METEOR, and CIDEr is more effective than others.
It is important to note that the improvement in METEOR indicates that our
methods can enhance the completeness of information and semantics in
responses. Additionally, the improvement in CIDEr further demonstrates that
our method can generate more crucial information.

Anchor | BLEU4 | CIDEr
---|---|---
VideoBART | 0.384 | 0.987
VideoBART+$\operatorname{Q}$ | 0.393 | 1.002
VideoBART+$\operatorname{VA}$ | 0.390 | 1.007
VideoBART+$\operatorname{H}$ | 0.395 | 1.010
VideoBART+$\operatorname{K}$ | 0.398 | 1.020
Table 6: Evaluation results of different modality ablation experiments on
AVSD10 dataset. $\operatorname{VideoBART+X}$ denotes the anchor is established
with $\operatorname{X}$.

####  5.5.3 Modality Anchor

To further explore the influence of different modalities, we establish a
series of experiments on different modality anchor token detection strategies,
and the results are shown in Table 6. Specifically, it is important to note
that all the different settings lead to performance improvement. Among the
settings, the question anchor shows a minimal performance gain which
demonstrates that our method needs multimodal knowledge anchor tokens to
reduce hallucination. Furthermore, other results also illustrate that the more
multimodal knowledge is used in anchor-enhanced generation, the greater
performance gains are obtained.

###  5.6 Case Study

Figure 6: The case study on the AVSD10 dataset, the multimodal knowledge-
related token is colored red.

We further examine the long sentence generation ability M2K-VDG on the AVSD
dataset, as depicted in Figure 6. We consider two examples from the
perspectives of auditory and visual hallucination, respectively. The results
confirm that our method can efficiently execute the VDG task in both ‘seeing’
and ‘hearing’, particularly in generating multimodal knowledge anchor tokens.
This demonstrates its substantial capability in reducing hallucination.

##  6 Conclusions

To reduce the varying hallucinations in the VDG task among different models,
we make effective use of the multimodal knowledge anchor tokens in the ground
answer, and propose M2K-VDG to achieve model-adaptive hallucination reduction.
Specifically, we measure the anchor degree by counterfactual effect and
enhance the training by anchor-enhanced loss. Through extensive experiments on
three VDG benchmarks, we substantiate the efficacy of our proposed approach in
comparison to various state-of-the-art methods.

## Limitations

Although our model is significantly more effective than the state-of-the-art
models, there is much room to improve the performance of anchor token
enhancement. For example, we only consider the anchor tokens for a single
specific model but ignore focusing more on the common anchor tokens shared by
various models. Furthermore, both two detection methods are limited to
locating the tokens related to specific modalities, but are unable to achieve
this goal from other aspects, such as spatial relations and existential
questions. In the future, we will continue to reduce the hallucination by
further employing anchor tokens.

## Ethical Consideration

We aim to propose a system to reduce hallucination by enhancing the multimodal
knowledge anchor and provide two methods to detect the anchor tokens
precisely. However, the performance of detection can effectively affect the
effectiveness of training and there are some risks of potential misuse. For
example, the wrong purpose of anchor detection such as violence and
pornography scene-anchor tokens detection can lead to an unhealthy multimodal
dialogue system. So, we would like to add a protection mechanism to prevent
the model from these kinds of words.

##  Appendix A Datasets

##### AVSD10

AVSD10333https://github.com/dialogtekgeek/AVSD-DSTC10_Official dataset (AlAmri
et al., 2019) is expanded from the Charades dataset Sigurdsson et al. (2016)
with multi-turns question-answering pairs, which concentrates more on the
daily life. There are three relevant datasets: AVSD7, AVSD8, and AVSD10
dataset, but the AVSD10 dataset is the most challenging among these datasets
so we only use AVSD10 as the illustration. The questions are proposed to
obtain both a comprehensive understanding of the video for overall
understanding and a detailed description of objective existence information.
As most answers consist of 5-9 words, we use BLEU, METEOR, ROUGE-L, and CIDEr
as the validation metrics to evaluate fluency and keyword generation
performance.

##### NExT-OE

NExT-OE dataset444https://github.com/doc-doc/NExT-OE (Xiao et al., 2021) is
constructed based on YFCC-100M dataset Thomee et al. (2015), which focus more
on outdoor activities. The questions focus on visual reasoning ability, which
consists of casualty, temporary reasoning, and descriptive ability, and the
answers are shorter than 4 words with certain semantics. Considering this, we
use the WUPS score to evaluate the semantic similarity between the answer and
the ground truth.

##### MUSIC-AVQA

MUSIC-AVQA dataset555https://gewu-lab.github.io/MUSIC-AVQA/ (Li et al., 2022)
is expanded from musical performance both real videos and synthesis videos.
The questions are more attentive to the audio reasoning, and the visual
reasoning questions are also contained. As each question has a certain answer
with 1 word, we use accuracy as the evaluation metric.

## References

  * AlAmri et al. (2019) Huda AlAmri, Vincent Cartillier, Abhishek Das, and et al. 2019.  Audio visual scene-aware dialog.  In _IEEE Conference on Computer Vision and Pattern Recognition_ , pages 7558–7567.
  * Chen and Deng (2023) Qiyuan Chen and Cheng Deng. 2023.  Bioinfo-bench: A simple benchmark framework for llm bioinformatics skills evaluation.  _bioRxiv_ , pages 2023–10.
  * Chen et al. (2023) Zhe Chen, Hongcheng Liu, and Yu Wang. 2023.  Dialogmcf: Multimodal context flow for audio visual scene-aware dialog.  _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , pages 1–13.
  * Cherian et al. (2022) Anoop Cherian, Chiori Hori, Tim K Marks, and Jonathan Le Roux. 2022.  (2.5+1)d spatio-temporal scene graphs for video question answering.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 36, pages 444–453.
  * Deng et al. (2023) Yifan Deng, Xingsheng Zhang, Heyan Huang, and Yue Hu. 2023.  Towards faithful dialogues via focus learning.  In _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 4554–4566, Toronto, Canada. Association for Computational Linguistics.
  * Fan et al. (2019) Chenyou Fan, Xiaofan Zhang, Shu Zhang, and et al. 2019.  Heterogeneous memory enhanced multimodal attention model for video question answering.  In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , pages 1999–2007.
  * Fayek and Johnson (2020) Haytham M. Fayek and Justin Johnson. 2020.  Temporal reasoning via audio question answering.  _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , 28:2283–2294.
  * Geng et al. (2021) Shijie Geng, Peng Gao, Moitreya Chatterjee, and et al. 2021.  Dynamic graph representation learning for video dialog via multi-modal shuffled transformers.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 35, pages 1415–1423.
  * Huang et al. (2022) Xin Huang, Hui Li Tan, Mei Chee Leong, and et al. 2022.  Investigation on transformer-based multi-modal fusion for audio-visual scene-aware dialog.  In _Proceedings of DSTC10 Workshop at AAAI-2022_.
  * Jiang and Han (2020) Pin Jiang and Yahong Han. 2020.  Reasoning with heterogeneous graph alignment for video question answering.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 34, pages 11109–11116.
  * Jin et al. (2023) Yao Jin, Guocheng Niu, Xinyan Xiao, and et al. 2023.  Knowledge-constrained answer generation for open-ended video question answering.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 37, pages 8141–8149.
  * Kim et al. (2021) Junyeong Kim, Sunjae Yoon, Dahyun Kim, and Chang D Yoo. 2021.  Structured co-reference graph attention for video-grounded dialogue.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 35, pages 1789–1797.
  * Lao et al. (2023) Mingrui Lao, Nan Pu, Yu Liu, Kai He, Erwin M Bakker, and Michael S Lew. 2023.  Coca: Collaborative causal regularization for audio-visual question answering.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 37, pages 12995–13003.
  * Le et al. (2023) Hung Le, Nancy Chen, and Steven C.H. Hoi. 2023.  C3: Compositional counterfactual contrastive learning for video-grounded dialogues.  In _Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue_ , pages 548–561, Prague, Czechia. Association for Computational Linguistics.
  * Le et al. (2020a) Hung Le, Doyen Sahoo, Nancy Chen, and Steven C.H. Hoi. 2020a.  BiST: Bi-directional spatio-temporal reasoning for video-grounded dialogues.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing_ , pages 1846–1859, Online. Association for Computational Linguistics.
  * Le et al. (2020b) Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran. 2020b.  Hierarchical conditional relation networks for video question answering.  In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , pages 9972–9981.
  * Lei et al. (2021) Jie Lei, Linjie Li, Luowei Zhou, and et al. 2021.  Less is more: Clipbert for video-and-language learning via sparse sampling.  In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , pages 7331–7341.
  * Li et al. (2022) Guangyao Li, Yake Wei, Yapeng Tian, Chenliang Xu, Ji-Rong Wen, and Di Hu. 2022.  Learning to answer questions in dynamic audio-visual scenarios.  In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , pages 19108–19118.
  * Li et al. (2021) Zekang Li, Zongjia Li, Jinchao Zhang, and et al. 2021.  Bridging text and video: A universal multimodal transformer for audio-visual scene-aware dialog.  _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , 29:2476–2483.
  * Liu et al. (2023) Hongcheng Liu, Zhe Chen, Hui Li, Pingjie Wang, Yanfeng Wang, and Yu Wang. 2023.  Msg-bart: Multi-granularity scene graph-enhanced encoder-decoder language model for video-grounded dialogue generation.  _arXiv preprint arXiv:2311.12820_.
  * Pasunuru et al. (2018) Ramakanth Pasunuru, Mohit Bansal, and et al. 2018.  Game-based video-context dialogue.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 125–136, Brussels, Belgium.
  * Schwartz et al. (2019) Idan Schwartz, Alexander G Schwing, and Tamir Hazan. 2019.  A simple baseline for audio-visual scene-aware dialog.  In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pages 12548–12558.
  * Shah et al. (2022) Ankit Shah, Shijie Geng, Peng Gao, and et al. 2022.  Audio-visual scene-aware dialog and reasoning using audio-visual transformers with joint student-teacher learning.  In _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing_ , pages 7732–7736.
  * Sigurdsson et al. (2016) Gunnar A. Sigurdsson, Gül Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. 2016.  Hollywood in homes: Crowdsourcing data collection for activity understanding.  In _European Conference on Computer Vision_.
  * Thomee et al. (2015) Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl S. Ni, Douglas N. Poland, Damian Borth, and Li-Jia Li. 2015.  Yfcc100m.  _Communications of the ACM_ , 59:64 – 73.
  * Wang et al. (2021) Mengmeng Wang, Jiazheng Xing, and Yong Liu. 2021.  ActionCLIP: A new paradigm for video action recognition.  _ArXiv preprint_ , abs/2109.08472.
  * Wu et al. (2022) Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar, and Juan Pablo Bello. 2022.  Wav2CLIP: Learning robust audio representations from CLIP.  In _ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing_ , pages 4563–4567. IEEE.
  * Xiao et al. (2021) Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. 2021.  Next-qa: Next phase of question-answering to explaining temporal actions.  In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_ , pages 9777–9786.
  * Xue et al. (2017) Hongyang Xue, Zhou Zhao, and Deng Cai. 2017.  Unifying the video and question attentions for open-ended video question answering.  _IEEE Transactions on Image Processing_ , 26(12):5656–5666.
  * Yamazaki et al. (2022) Yoshihiro Yamazaki, Shota Orihashi, Ryo Masumura, and et al. 2022.  Audio visual scene-aware dialog generation with transformer-based video representations.  _ArXiv preprint_ , abs/2202.09979.
  * Yoon et al. (2023) Sunjae Yoon, Dahyun Kim, Eunseop Yoon, Hee Suk Yoon, Junyeong Kim, and Chnag D Yoo. 2023.  Hear: Hearing enhanced audio response for video-grounded dialogue.  _arXiv preprint arXiv:2312.09736_.
  * Yoonseok et al. (2022) Heo Yoonseok, Kim Gyunyeop, Yoo Eunseok, Lee Seungsoo, Jeong Eunseo, and Kang Sangwoo. 2022.  Interpretable multimodal dialogue system with natural language-based multimodal integration.  In _Proceedings of DSTC10 Workshop at AAAI-2022_.
  * Yu et al. (2019) Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019.  Deep modular co-attention networks for visual question answering.  In _2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , pages 6274–6283.
  * Yun et al. (2021) Heeseung Yun, Youngjae Yu, Wonsuk Yang, Kangil Lee, and Gunhee Kim. 2021.  Pano-avqa: Grounded audio-visual question answering on 360° videos.  In _2021 IEEE/CVF International Conference on Computer Vision (ICCV)_ , pages 2011–2021.
  * Zhao et al. (2022) Xueliang Zhao, Yuxuan Wang, Chongyang Tao, Chenshuo Wang, and Dongyan Zhao. 2022.  Collaborative reasoning on multi-modal semantic graphs for video-grounded dialogue generation.  In _Findings of the Association for Computational Linguistics: EMNLP 2022_ , pages 5988–5998, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.