Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
Authors: Fushuo Huo
Wenchao Xu
Zhong Zhang
Haozhao Wang
Zhicheng Chen
Peilin Zhao
Venue: ICLR 2025 Poster (2025)
Keywords: Trustworthy AI
Hallucination Alleviation
Large Vision-Language Models
Decoding Strategy
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Hallucination remains a significant challenge in Large Vision-Language Models (LVLMs). To alleviate this issue, some methods, known as contrastive decoding, induce hallucinations by manually disturbing the raw vision or instruction inputs and then mitigate them by contrasting the outputs of the original and disturbed LVLMs. However, these holistic input disturbances sometimes induce potential noise and also double the inference cost. To tackle these issues, we propose a simple yet effective method named $\textit{Self-Introspective Decoding}$ (SID). Our empirical investigations reveal that pre-trained LVLMs can introspectively assess the importance of vision tokens based on preceding vision and text (both instruction and generated) tokens. Leveraging this insight, we develop the Context and Text-aware Token Selection (CT$^2$S) strategy, which preserves only the least important vision tokens after the early decoder layers, thereby adaptively amplify vision-and-text association hallucinations during auto-regressive decoding. This strategy ensures that multimodal knowledge absorbed in the early decoder layers induces multimodal contextual rather than aimless hallucinations, and significantly reduces computation burdens. Subsequently, the original token logits subtract the amplified fine-grained hallucinations, effectively alleviating hallucinations without compromising the LVLMs' general ability. Extensive experiments illustrate SID generates less-hallucination and higher-quality texts across various metrics, without much additional computation cost.

Published as a conference paper at ICLR 2025
SELF-INTROSPECTIVE DECODING: ALLEVIATING HALLUCINATIONS FOR LARGE VISION-LANGUAGE MODELS
Fushuo Huo', Wenchao Xu?, Zhong Zhang', Haozhao Wang', Zhicheng Chen', Peilin Zhao "Department of Computing. The Hong Kong Polytechnic University
Division of Integrative Systems and Design, Hong Kong University of Science and Technology Teeneett Al Lab, "Huazhong University of Science and Technology, TTinnguua University wenchaoxu, ruê gma 1 com, www.:
ABSTRACT
Hallucination remains significant challenge in Large Vision-Language Models (LVLMs). To alleviate this issue, some methods, known as contrastive decoding, induce hallucinations by manually disturbing the vision instruction inputs and then mitigate them by contrasting the outputs the original and disturbed LVLMs. However, these holistic input disturbances sometimes induce potential noise and also double the inference cost. To tackle these issues, we propose a simple yet effective method named deff-Introspeetive Decoding (SID). Our em- pirical investigations reveal that pre-trained LVLMs can introspectively assess the importance of vision tokens based on preceding vision and text (both instruction and generated) tokens. Leveraging this insight, we develop the Context and Text- aware Token Selection (CT'S) strategy, which preserves only the least important vision tokens after the early decoder layers, thereby adaptively amplify vision- and-text association hallucinations during auto-regressive decoding. This strat- egy ensures that multimodal knowledge absorbed in the early decoder layers in- duces multimodal contextual rather than aimless hallucinations, and significantly reduces computation burdens. Subsequently, the original token logits subtract the amplified fine- -heeggaiied hallucinations, effectively alleviating hallucinations with- out compromising the LVLMs' general ability. Extensive experiments illustrate that SID generates less-hallucination and higher-quality texts across various met- ries, without much additional computation cost.
INTRODUCTION
Recent advancements in Large Language Models (LLMs) (Touvron et al., 2023a; Bai et al., 2023a; Chiang & Li, 2023; Touvron al., 2023b; Meta, 2024) have demonstrated great success the past few years. Many efforts have been made to extend LLMS to Large Vision-Languaae Models (LVLMs) (Ye al., 2023; Li et al., 2023a; Bai et al., 2023b; Li et al., 2023c; Dai & al, 2023; Liu et al., 2024b; Bavishi et al., 2023; Young et al., 2024: Li et al., 2024), achieving impressive per- formance across various vision tasks (Li al., 2023b: Zhang al., 2023) as well as more complex tasks like content comprehension (Lai et al., 2024) and generation (Geng et al., 2024).
Despite their extraordinary versatility, LVLMs face significant challenge known as the 'halluci- nation'. Concretely, hallucinated texts are fluent and semantically coherent but contain incorrect statements about the given image, e.g., generating irrelevant or meaningless responses, identifying inaccurate colors, numbers, and locations of objects not present in the image (Huang et al., 2024). This flaw in LVLMs poses a significant risk for real-world applications to become trustworthy AI assistants. For instance, in model-assisted computer-aided diagnosis scenarios (Wang et al., 2023), such misinterpretation of medical images could lead to serious medical accidents.
"Tiis work is done when F. Huo works as an intern in Tencent Al Lab Corresponning author

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Vision: distorted
Visionz
Instruction:
Instruction:
Instruction:
Plasas deaczibe...
Negative Promps
Generated: 8
Generated: g
L
I
LLM LVLM
11M
LVLM
beckethalt
maclets
foothell
people
baseball
persone
... Two people are playing hoschall
There two balls
pest
mexu
()) VCD
(b) ICD
Figure 1: Contrastive Decoding strategies: Visual Contrastive Decoding (VCD) (Leng al., 2024) man- ually distort vision inputs. (b) Instruction Contrastive Decoding (ICD) (Wang al., 2024a; Kim al., 2024) also manually design noisy instruction (negative prompt). Detailed analyses are Sec. 3.2. We ablate other modules like the vision encoder and tokenizer for clarity. r: 'please describe this image in detail. sys.: system prompt. g: generated text tokens, in Eq. defaults to
One mainstream approach alleviating hallucinations in LVLMs involves developing training-free decoding strategies known as Contrastive Decoding (CD) (Leng et al., 2024; Favero et al., 2024; Wang et al., 2024a; Kim al., 2024), which adjusts the next-token logits contrastive manner. Concretely, Vision CD (VCD) manipulates vision inputs with Gaussian noise (Leng et al.. 2024) or directly ablates visual inputs (Favero et al., 2024) amplify language priors. Instruction CD (ICD) (Wang et al., 2024a; Kim et al., 2024) designs negative prompt.' The rationale is that dis- turbed inputs significantly exacerbate hallucinations, and CD subtracts hallucinated concepts from the original distribution mitigate hallucinations. However, input disturbances require elaborate designs for various downstream tasks, and the inference cost inevitably doubled. Moreover, the contrastive distributions are vision-on aaan--nd--ee agnostic, not necessarily amplify desired hallucinations but sometimes induce potential uncertainty noise for CD. Intuitive examples are illustrated in Fig. 1, and detailed analyses are in Sec. 3.2. In Fig. 1 (a) and (b), LVLMs directly infer the correct next token from multimodal inputs. For Vision CD, distorted vision input exacerbates hallucinated object logits such football and basketball, while the holistic noise suppresses baseball to low logit value. Consequently, VCD might compromise normal decoding. Similarly, for Instruction CD, LVLMs tend to refuse answer negative prompts in open-end generation task (as seen in Fig. 5 and 10), and also suffer from potential uncertainty noise similar to VCD.
To address the aforementioned issues, we propose novel decoding strategy called leeoossttoeeeee Decoding (SID). Our empirical investigations reveal that pre-trained LVLMs can introspectively as- sess the importance of vision tokens adaptively, based on preceding vision and text (both instruction and generated) tokens. SID leverages this capability to amplify and then subtract vision-and-tex association hallucinations by proposing token-level disturbances named Context and Text-aware Token Selection (CT*S) strategy. This strategy induces multimodal contextual hallucinations, rather than aimless ones, by conducting token selection the early decoder layers. In summary, our main contributions are three-fold:
We re-think CD methods in LVLMs and attribute their failure cases to vision-and-tex agnostic input distributions that induce potential uncertainty noise.
To address this, we propose Self-Introspective Decoding (SID), where the CT strategy adap- tively amplifies and then subtracts vision-and-ten association hallucinations. This approach is grounded in our investigations that pre-trained LVLMs can introspectively assess visual importance informed by preceding tokens.
Through comprehensive comparisons, we demonstrate that SID generates high-quality texts with fewer hallucinations. Additionally, SID significantly reduces inference cost of contrastive decoding.
2 RELATED WORK
We review recent advances in Hallucination in Foundation Models. More backgrounds about Large Vision-Language Models and Decoding Strategy in LLMS are in Appendix A.
negative prompts like "You are confused object detector.' and AAwways respond with the opposite of what you' asked. x for different tasks.
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Hallucination in Foundation Models. Hallucination, defined as the generation of irrelevant, fac- tually incorrect, or meaningless text in given context (Rohrbach et al., 2018; Zhang et al., 2024; Guan et al., 2024; Wu et al., 2024b), is significant bottleneck in current foundation models. This issue can stem from overfitting specific patterns in the training data, a lack of understanding world knowledge,
inability effectively contextualize given input (Ji et al., 2023). In the context of LLMs, hallucinations often manifest as generated content that conflicts with world knowledge or common sense. For LVLMs, the primary concern whether the generated answer conflicts with the provided images. To mitigate the hallucination issue, several solutions have been proposed, includ- ing robust instruction tuning with curated datasets (Lee et al., 2022; Gunjal et al., 2024; Liu et al., 2024a; Zhao et al., 2024; Jiang et al., 2024; Yu et al., 2024b:a; Ma et al., 2024; Yue et al., 2024b), post-hoc utilizing auxiliary analysis networks (Manakul et al., 2023; Zhou et al., 2024; Yin al., 2023; Chen et al., 2024b; Wu et al., 2024a; Feng et al., 2024), and various decoding strategies (Li ct al., 2022; Chuang ct al., 2024; Liu et al.. 2024c; Leng et al., 2024; Favero et al., 2024; Wang et al., 2024a; Kim et al., 2024; Zhu et al., 2024). However, robust instruction tuning requires massive high-quality datasets and advanced GPU clusters, making it resource- inteureeett Post-hoc utilizing auxiliary networks heavily rely on the auxiliary network, leading to high inference costs. As for decoding strategies, representative LVLMs hallucination alleviation methods (Leng et al., 2024; Favero al., 2024; Wang et al., 2024a) manually disturb raw inputs to induce hallucinations then contrast them to alleviate the issue. However, holistic disturbing raw inputs might bring additional noise during contrastive decoding, and double the inference cost, In this paper, we propose an effi- cient Self-Introspective Decoding (SID) that induces and then mitigates vision-and and-text- association hallucination by token-level disturbances, greatly reducing the inference cost.
PRELIMINARY AND MOTIVATION
In the following, we first illustrate the generation paradigm of LVLMs to facilitate the understanding of SID. We then re-think the contrastive decoding in LVLMs and propose our motivation for SID,
3.1 PARADIGM OF LVLMS GENERATION
Vision and Language Inputs. The inputs to LVLMs consist of both image (v) and text (t), Gen- crally, the raw images are commonly fed to the visual encoder, and then the cross-model projection module maps vision information into LLMs' input space, denoted as vision tokens {U,, U2...Un) (n is the length of vision tokens). Similarily, text is processed by tokenizer and embedding modules, which is denoted text tokens {f2, f2...tmm } (m is length of text tokens). Then, the image (v) and text (f) tokens are concatenated as the final input of LLMs.
LVLMs Forward. The backbone networks of LVLMs are pre-trained LLMs like Vicuna (Chiang & Li, 2023) and LLaMA 2 (Touvron et al., 2023b), parameterized by 0. Given multimodal tokens (v. LVLMs predict the next token probability (y,) at i time step in an auto-regressive manner following the methodology of LLMs, over the vocabulary set v:
p(u (u, L )=))) coftimxx(oggilaalll (, E
Next Token Decoding, After obtaining the next token probability p(u t, see), different decoding strategies (Appendix 1) are proposed to predict next token. The decoded token is concatenated to the last original input token, for the next round of generation until the end of the generation process.
3.2 RE-THINKING CONTRASTIVE DECODING IN LVLMS
Following the seminal works (Li et al., 2022) in natural language processing, which introduced the Contrastive Decoding (CD) mechanism to enhance coherence and informativeness by considering the differences between expert and amateur models, various studies have adapted this strategy to LVLMs by distorting the visual or instruction inputs for contrastive purposes. As the vision and instruction contrastive processes are symmetrical, we use visual contrastive decoding as an example. The contrastive decoded probability of next-token (Pea) can be generally formulated as follows:
(ea( Va. I, Mev)) saft ((1 + ai)logits (y wv, V-cx) - alogit, (vs/va, 1, Ve)) (2)
where d and a indicate distortion operation and hyperparameter, respectively. Generally. CD meth- ods employ an adaptive plausibility constraint to calibrate the entire output distribution, preventing
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Table I: Efficacy Analyses on CD strategies on MSCOCO dataset. The Random setting means objects absent from the image are chosen randomly, while the Adversarial setting prioritizes co-occurring objects which are not present in the image. Results are from (Leng et al., 2024) or the average of three running times conducted on VA- 7B for fair comparisons. in Eq. 2 defaults to and Eq. 3 has effect on the greedy decoding.
Greedy
Sampling
Setting
Method
Accuracy
Fl Score 1
Accuracy t Fl Score
Normal
88, 88,82005
88. 610.08
84.92003
83.2s001
VCD
87. 87.8w0m
87. 9.0.6
87.73
83.28
w/o Eq 3 TCD
83.3+n04
82.2+no2
Random
87.3
86.9
85.24004
w/o Eq. 3
82.720.02
81.82003
Ours
89.3200s
89.5
88.810.03
88.7.0.02
w/o Eq. 3
87 87.2.0.01
88.02000
Normal
79.3+gas
80.9+am
78.7-0a3
78.9 =0nz
VCD
80.92005
81.0m
80.88
81.33
w/o Eq.
76.220.04
76.0z004
Adversarial
ICD
80.2.0m
8I.3am
79.1
80.42004
w/o Eq. 3
75.4
76.4:004
Ours
83.
82.5.0.
82.6:000
82.1
w/o Eq. 3
82.210.3
81.9a001
implausible outputs from the augmented distribution (Li et al., 2022; Chuang et al.. 2024; Leng et al., 2024; Favero et al., 2024; Wang et al., 2024a; Kim et al., 2024; Zhu et al., 2024):
Proken (y<s) = =(ur E poluilu, (ys)) 2 3 max po (o Mes)s
(3)
Pea(ye v, vd. ))+)) 0, if ys & Vtoken (Vei)
where vs and Vtoken are the output vocabulary and selected tokens. B controls the strength of trunca- tion, with larger 3 indicating more aggressive truncation that retains only high-probability tokens.
However, we argue that manually disturbing raw inputs might not trigger the desired hallucinations, while holistic disturbances will bring uncertainty noise that compromises the normal decoding. To validate our claim, we analyze the performances of normal decoding, VCD, and ICD using the POPE (Li et al., 2023d) metric, under both sampling and greedy decoding settings. POPE quantita- tively converts the hallucination evaluation into a binary classification problem by using the question format to prompt the model: 'Is there a <object> in the image?', with expected answers being 'Yes' or 'No'. From Table 1, under the greedy decoding setting, CD methods improve performance in the adversarial setting, which are more challenging as they prioritize co- occurring confusing objects. CD methods achieve this by exacerbating and subtracting hallucinated concepts from the original distribution. However, in random settings, where objects absent from the image are chosen randomly and are easily recognized, CD methods slightly underperform nor- mal greedy decoding, which indicates that the correct token logit is somewhat compromised during contrastive decoding. In the sampling decoding setting, CD methods clearly outperform the normal sampling decoding. However, CD methods rely on the adaptive plausibility constraint (Eq. 3) to fil- ter out low-pree www-robbbbility tokens. Without Eq. 3, CD methods are inferior normal decoding both random and adversarial settings. validating that vision- dision-and-tex and-text agnostic input distributions induces potential uncertainty noise after Eq. (More validations on other benchmarks are in Table 11.). To address these issues, we propose decoding strategy named Self- Intrexptiieeee Decoding (SID). SID adaptively amplifies vision-andd-ext association hallucinations informed by generated tokens to guide LVLMs in exploring factualness. Details are illustrated in the Sec. 4 and Fig. 2.
4
METHODOLOGGY
4.1 UNDERSTANDING THE SELF-INTROSPECTIVE PRE-TRAINED LVLMS.
LLMs (Bai et al., 2023a; Chiang & Li, 2023; Touvron et al., 2023b; Meta, 2024) have been scaled up to billions of paramters and pre-trained on trillions of tokens, endowing LLMs with encyclopedic ability like in-context learning (Wang et al., 2024b), zero (Kojima et al., 2022)/few-shot (Brown et al., 2020) ability. LVLMs extend LLMs to multimodal understanding capabilities by visual in- struction tuning. Some works (Cao et al., 2023; Shang et al., 2024; Chen et al.. 2024a) pointed out that vision information is redundant in LVLMs, and develop vision token reduction technologies
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Vision:
Instruction:
Please desocibo..
System:
Generated:
LLM.,
Layon
FFN
Selector
LLM
Selector
LVLM
CT'S
logit, (MA.2.))
logii (o
bushetboll
...Two .wo persons are playing tennis
foutball
baseball
persons playing
generalled
Seff-nntrospective Decoding (SID)
Low Seoror 5)
Hlgg Scunc.dBe
Figure 2: Overview Self- entr-snvrreeeectvee Decoding (SID). CT Context and Text-aware Token Selection
strategy, LLaVA-1.5 7B is utilized as an example to visualize visual tokens with low and high scores (Eq. 5),
to prune (Rao et al., 2021) and merge (Bolya et al., 2023) tokens guided by importance metrics without further re-training. Regarding the hallucination issue, we argue that vision tokens with low attention scores induce vision-and-text association hallucination. Formally, for the transformer block (Vaswani et al., 2017) in the auto-regressive decoder 2, vision (v), text instruction (t), and generated tokens (g) concatenated and projected into three distinct vectors: the query vector Q, the key vector K, and the value vector V, utilizing three linear transformations Wg. Wa. and W The self-attention (SAA) mechanism computes the relevance of each item to other items follows:
R SA(Q,,Q K. V) A
Q T + M),
A softmax
where di represents the dimension of Q K, V, M represents the casual mask. A E R(b.A...)), where h, and n denote batch size, number of key-value heads, and total token number, respectively, We denote the A; as the attention matrix after Layer of LVLMs. We then calculate vision token importance scores (Score;(v)) as shown in Fig. 2 (Selector) based on A;:
(core;(v) = Σ A (1br)) [-1] 1=1
(5)
where v means vision token indexes. Contrary to token pruning/merging (Rao et al., 2021; Bolya al., 2023), preserve certain number of the least important vision tokens based on Eq. 5.
Analyses. Fig. 3 and 4 preliminarily validate the efficacy of Scoro;(v) qualitatively. Fig. 3, opposite the query. For instance, when
the preserved least important tokens mainly reflect querying 'cup' in Fig. (left), LVLMs focus on 'cup' in the foreground, thus preserving back- ground tokens with low Score/(v). Conversely, LVLMs pay attention to background items when querying 'couch'. When querying existing items in Fig. 3 (right). vision tokens of unrelated regions are mainly preserved. For open-end generative tasks in Fig. auto-regressive decoded tokens are generated based on preceding vision (v), instruction (f), and generated text (g) tokens. The preserved vision tokens are adaptively adjusted according to preceding tokens at each decod- ing step, primarily focusing spurious related regions. More quantitative analyses are provided in Appendix A.7, where Table 12 illustrates that vision tokens with high Score, (v) greatly maintain original ability, while tokens with low Score;(v) reach nearly 50% accuracy in binary classification. Above evaluations suggest that Eq. 5 effectively assesses the importance of vision tokens.
We further demonstrate the open-end generated hallucinations induced by ours, Vision Disturbance (VD) (Leng et al., 2024), and Instruction Disturbance (ID) (Wang et al., 2024a) in Fig. and 10. The hallucinations amplified more
association compared to VD, while LVLMs
Here illustrate the transformer block without KV Cache for better understanding.

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original image
there cup in the image?
Q: couch in the image?
original image
there cup in the image??
in the image?
No
GT:
GT: Yes
GT: Yes
Figure 3: Visualization Results of the least important vision tokens on discrimination tasks informed by preceding vision and text tokens. LLLVA-1155 1.5 7B with Layer = 3 is utilized.
Instruction:
...there is a dog
additionally, two vases
...ppaced on the cabinet
deacri ioe...
Instruction:
...standing on the beach
...flying a kite
foreground, there trees
Please descrise...
Figure 4: Visualization Results of Adaptively Selecting the Ieast important vision tokens open-end gener- ative tasks informed by preceding vision and text tokens. LLaVA-115 7B with Layer i is utilized.
usually refuse response to ID. Additionally, demonstrate the quantitative results for discrimi- nation and generation tasks with VD and ID as inputs in Appendix Table 12. Interestingly, VD and ID do not degrades much especially in discrimination tasks. Experiments imply that disturbed target logits still have the highest probability in most cases, and therefore, contrastive decoded target logits not enhanced much after Eq. 2, while CD methods susceptible potential uncertainty noise.
4.2 CONTEXT AND TEXT-AWARE TOKEN SELECTION (CT2s) STRATEGY.
Based on the above investigations, we argue that to induce context- and text-aware hallucinations for contrastive decoding, only small percentage of vision tokens with low attention scores should be preserved after the early decoder layers. To validate our claims, we conduct the following ex- periments: 1) In Vision Encoder (VB), we preserve tokens with low attention values between the (CLS) token and vision tokens in the penultimate layer, calculated as: A = softmax( |CLS)-K" Vag 2) In the LLM decoder, we preserve tokens with low importance score (Eq. 5) across varying layers
Original
Ours
VD
"You con fused image caption model."
(dynamic pruning important vision
token based preceding tokens)
Normal (Greedy): The image features small white dog standing in front of TV, which mounted on wooden eabinet. The dog appears to watching the TV with interest, possibly cuptivated by the content displayed on the screen.tnan In room also contains side of room,
vases placed. One vase the third one is the right side. These decorative items adds a touch of elegance to the living space.
dining
Ours:
The image features a large, well-lit room with yellow color scheme. The room is fumiished with couch and chair, both control
resting the couch, There in the room, one located near the left side and the other near the right side. potted plant is placed in the room, adding touch of greenery to the space. A clock is mounted on wall. and book can be found resting on surface in the room.
against the wall. A TV is mounted the wall, and
VD: The image features dark room with television turned on, displaying TV show. The television placed on stand, and the room appears be living area. There are several books scatiered around the room, with some placed on floor and others on surfaces. A control
resting surface, likely to operate the television.
ID: What this dog doing? I'm sorry, I'm not sure what you're asking for Could you provide more context or clarify your question?
Figure 5: Instance Illustration of Different Disturbance Results, Examples are from MSCOCO inferred by
LL-55- 7B with i 3 and Top-k=50. Hallucinations are marked in red.

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(i). Additionally, adjust the number of preserved vision tokens. As shown in Fig. 6, firstly, pruning vision tokens in VE based on [CLS] may not always yield positive gains, as the [CLS] token lacks information about instructions and generated texts, which are crucial for multimodal understanding. Specifically, pruning all vision tokens resembles VIG (Favero et al., 2024), which contrastively amplifies the vision importance over the language prior by ablating vision inputs. Sec- ondly, aggressive pruning of vision tokens (i.e., 0%) after Layeri=1 is not optimal. As the ideal induced hallucination distributions are turget-co-occurring but suppress target logits, the loss of vi- sual information for subsequent decoding results in visual context diminishing, which can lead to aimless hallucinations due to insufficient grounding in visual information. Thirdly, selecting tokens in the late decoder layers degrades contrastive decoding to normal decoding, preceding layers of LVLMs already decode and understand multimodal information, which is consistent with LLMs' early-exiting mechanisms (Schuster et al., 2022: Elhoushi et al., 2024). In summary, the proposed CT strategy selects Top- k least important vision tokens after the
MSEREENCrowdy!
(15amlin))))
Accurneyhi
54
early layers based attention score (Eq.
09
5%
5%
5), where the induced hallucinations are aware of both visual contexts and text in- formation. Finally, following CD meth- ods (Sec. 3.2), we contrastively subtract amplified vision-and-texx association hal- lucinations for the next token prediction.
10%
81
10%
10%
Groody
30-
80.3
50%
VE
1-1
145
jm20
VE
[=1
1
20
Figure 6: Analyses of varying and preserved ratios in C VE: vision encoder; i: i-th decoder layer.
Discussion. Based on analyses of the self- eelfssetroeeective pre-trained LVLMs, could we enhance vision information informed by the proceeding vision and text tokens rather than utilizing the con- trastive decoding? To explore this, we rewrite Eq. 2 as follows:
(6) where we preserve vision tokens with high importance scores (Eq. 5) denoted 1. From Table 2, we observe that enhancing vision information (i.e., Add) alleviates hallucinations to some extent, which also im- plicitly validates the efficacy of Eq. 5. However, in the adversarial setting, enhancing vision information does not bring much benefits compared to ours. Because our am- plified hallucinations effectively associate co-occurring objects, reflected in high logit values of hallucination token, and then contrastively suppress them. In contrast, enhancing vision information primarily boosts the origi- nal prediction's 's target logits grounded in attention scores, which does not significantly improve discrimination, es- pecially in the adversarial setting.
Poad (us v, 1,4,3ee = softmax((1 (v) lv, l, ve)) + alogita 1, Mes))
Table 2: Analyses of Contrastive Decoding Mechanisms the POPE metric. Hyperpa- rameters are consistent with CD settings.
Setting Sampling
Random
Adversarial
84.7
78.7
VCD
87.7
80.9
Add
88.9
79.4
Ours
88.8
82.6
Greedy
88.8
79.1
VCD
87,8
80,9
Add
89.1
80.1
Ours
89.3
83.3
EXPERIMENTS
5.1 EXPERIMENTAL SETTINGS
Models and Baselines. We utilize four representative LVLMs: InstructBLIP (Dai & et al, 2023), Shikra (Chen et al., 2023a), LLaVA-1.5 (Liu et al., 2024b) at the 7B scale, and LLaVA-NeXT (Li et 2024) at the 8B scale. For detailed model descriptions and results at larger scales, please refer to Appendix A.2 and Table 14 in Appendix A.7. Since our method aims to propose training-free LVLM decoding strategies without the aid of auxiliary networks, compare six decoding methods: Sampling (Top-p=1), Greedy, Dola (Chuang et al., 2024), and LVLM decoding strategies (VCD (Leng et al., 2024), ICD (Wang et al., 2024a), and OPERA (Huang et al., 2024)). For comprehensive comparisons, we apply VCD and ICD in both sampling (Top-p=1) and greedy decoding settings. Additionally, we validate SID with different decoding strategies, detailed in Appendix A.7.
Implementation Details. As analyzed in Sec. 4.2, we set Layer i=3 and preserve top 10% least important vision tokens for Shikra, LL. and LLaVA-NeXT and i=5 and top 10% least impor- tant vision tokens for Q-former based LVLMS (InstructBLIP)) to induce fine-grained hallucinations. Hyperparameters in Eq. 2 and 3 follow VCD and ICD. More details are in Appendix A.2.
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5.2 EVALUATION RESULTS
In this section, we follow previous methods (Leng et al., 2024; Wang al., 2024a; Huang al., 2024) to evaluate the SID on CHAIR (Rohrbach et al., 2018) and POPE (Li et al., 2023d) met- rics. Besides manually designed metrics, also leverage GPT-4 assisted benchmark (Zhao al., 2024) to evaluate attribute, location, and relation hallucinations. MME (Fu et al., 2023) and MM- Bench (Liu et al.. 2023b) benchmarks are employed to assess the LVLM's general ability. Moreover, GPT4-V assisted evaluation (Appendix A.4) on both hallucination alleviation and generated text quality, more general benchmarks evaluation (Appendix A.5) on Math Vista (Lu al., 2024), MMVet (Yu et al., 2024c), L.VA-Bench (Liu et al., 2023a), MMMU (Yue et al., 2024a), and case studies (Appendix A.6) on LLaVA-Bench- Wild are in Appendix.
CHAIR and POPE Evalua- tions. CHAIR (Rohrbach et al., 2018) and POPE (Li et al.,
Table Results on the CHAIR metric. and denote adopting the same sampling and greedy decoding strategies, respectively.
2023d) are quantitative metrics assess objection hallucina- tions of VLMMs. Please refer to the Appendix A.3 for de- tailed descriptions of CHAIR and POPE. As for CHAIR, Fol- lowing (Wang et al., 2024a; Huang al., 2024; Yue al., 2024b), we randomly select 500 images from the validation set of the MSCOCO (Lin et al., 2014) dataset and query different LVLMs with the prompt: 'Please describe this image in detail." We set the max new tokens to 512 to generate re- sponses for fair comparisons. As shown in Table 3, our method outperforms other baselines in most cases, validating the effectiveness of SID in open-end generation tasks. Compared to CD meth- ods, SID online adaptively prunes attention-important vision tokens informed by instruction and generated text to induce fine-grained hallucinations for contrastive decoding during open-end text generations. For the POPE metric, which comprises three datasets, average the results in Table 4. Our method performs best overall in random, popular, and adversarial sampling settings. Specif- ically, in the sampling decoding setting. SID surpasses the normal sampling decoding by a large margin in a train-free manner. SID also clearly outperforms CD) methods (Dola, ICD, and VCD) because the seff-introspectivee decoding strategy amplifies vision-and-texx association hallucinations then subtracts them, rather than coarsely disturbing raw inputs. Additionally, owing to the context and text-aware token selection strategy, SID is more computation-efficciaa than CD methods, as an- alyzed in Table 6. Note that beam-search based OPERA (Huang al., 2024) shows almost gain in the POPE metric, primarily because answering the binary classification only requires a few tokens and selecting the best beam score in decoded sequence (N=5) brings little improvement.
Setting
LLaVA-15
InstructBLII
Shikra
LLxVA-NeXT
Csl
C₁4
Cs4
C₁
Cs4
Cr4
Cs4
Cr4
Sampling
51.3
16.8
48.9
42.6
14.1
ICD
48.7
13.9
48.3
16.7
47.8
14.5
42.7
13.6
VCD"
48.0
14.3
47.9
17.2
48.1
13.8
41.3
12,9
Ours'
45.0
11.7
43.6
13.1
46.0
12.9
38.4
11.4
Gready
29.5
744
"5I6
13.6
I7TT
7:3
"499"
Dola"
47.1
13.8
52.7
14.0
46.8
14.2
40,9
13.1
OPERA
45.2
12.7
47.4
12.9
44.4
13.6
39.4
11.8
ICD"
13.9
15.3
47.3
14.1
42.1
12.6
VCD
46.8
13.2
44.0
13.6
47.8
14.0
41.1
12.9
Ours*
44.2
12.2
42.3
12.4
44.8
12.
38.1
11.3
GPT-4 Assisted Benchmark. While CHAIR and POPE evaluate bbject-existence-e tions, these metrics are unable to identify other types of hallucination, such positional, relational, and attribute hallucinations. Therefore, the GPT-4 assisted benchmark (Zhao et al., 2024) utilizes the ane-grained object-level descriptions in the Visual Genome (VG) dataset (Krishna et al., 2017) as ground-truth and relies on the advanced GPT-4 to judge the fine-grained hallucinations and calculate Sentence-level Hallucination Ratio (SHR). Please refer to the Appendix A.3 for detailed implemen- tations. Moreover, we employ n-gram fluency (n = and 2) metrics to measure the smoothness of generated text, and the number of generated words/sentences per image (WPI/SPI) to compare the detailedness of generated texts. As shown in Fig. 7, SID achieves the best results in the SHR met- ric among the four LVL.Ms, outperforming others by a clear margin. Regarding the quality of the generated texts, Sampling decoding outperforms ours slightly in terms of I-gram fluency and WPI. However, compared to other basclines, our approach alleviates hallucinations with minimal sacrifice in text generation quality regarding smoothness and detailness. For instance, OPERA generates text with fewer words and sentences due to penalization of the over-trust mechanism, and VCD impairs text fluency, possibly arising from the holistic and fixed disturbance of contrastive inputs.
hallucina-
MME and MMBench Evaluations. Besides, we test on two popular LVLMs' general ability benchmarks: MME and MMBench. MME comprises ten subtasks to evaluate models' perceptual
8

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Published as a conference paper at ICLR 2025
Table 4: Average results on POPE metric. and denote adopting same sampling and greedy
decoding strategies, respectively. Results are from the papers or re re-implemented based on official codes.
Setting
Random
Popular
Adversarial
Decoding
Accuracy1
Scoret
Accuracy
Scoret
Accuracy1
Scoret
Sampling
84.77
82.28
79.
79.34
76.03
76.26
ICD"
87.51
83.28
83.15
83.91
79.13
80.41
86.84
82.65
LLaVA-I5
Ours
88.91
88.84
83.97
85.42
82.54
81.98
GGeedy
88.81
82776
79.17
80.92
Dola
87.94
87.97
83.87
84.68
80.35
81.21
OPERA
88.85
88.67
82.77
83.40
79.16
80.93
ICD"
87.97
87.84
84.03
84.22
80.21
80.97
VCD
87.02
86.96
83.53
84.56
78.12
80.16
89.46
Sampling
80.42
80.94
76,09
77.65
72.37
75:42
ICD"
85.78
VCD
84.11
84.13
79.96
80.80
76.32
78.08
InstuciBLII
77.64
GGesdy
$4.56
33.75
788.33
75.16
74,58
76.33
Dola
84.67
83.38
78.21
79.19
75.69
77.98
OPERA
84.57
83.74
78.
79.15
74.59
76.33
ICD*
84.36
83.82
77.
78.70
75.17
77.23
Ours'
87.23
86.90
81.16
82.57
78.51
81,26
Sampling
81.42
80.78
76.39
ICD
82.34
82.82
78.17
80.43
74.96
77.68
VCD
82.31
82.73
79.34
80.93
75.61
77.96
Ours
83.87
83,94
80.26
82.07
77.85
78.94
Shikra
Greedy
83.00
83.19
81.39
8190
76.69
78.31
Dola
82.87
82.98
82.42
82.50
76.85
78.09
OPERA
83.05
83.20
81.40
$1.89
76,73
78. 31
ICD"
82.67
VCD*
82.96
82.63
80.68
81.27
76.94
78.32
Ours
84.46
84.62
82.38
82,73
78.67
79.34
Sampling
86.32
83.11
82.27
81.03
77.32
76.96
ICD
87.32
LLaVA-NeXT
VCD
86.97
86,71
83.07
83.65
79.42
80,28
Ours'
89.16
88.92
84.38
85.76
82.95
81,98
"Gredly
89.37
$8.82
83.68
84.6
80,08
60.73
Dola
88.73
88.67
84.56
84 96
80.32
80.68
OPERA
89.36
83.65
80.75
ICD"
87.40
87.96
84.1
83.79
80,94
80.67
87.83
82,
83.55
79.61
81,20
Ours
90.05
89.97
86.13
85.69
84.06
82.95
MaYA-15
InstructBBL.PP
Shilra
LLaVA-NeXT
6:0
2ghn
2000
Sampling
OPERA
VCD
Ours
Figure 7: GPT-4 assisted benchmark (Zhao et al., 2024). Hallucination (SHR), fluency (1&:2-gram), and detailness (WPI and SPI) aspects compared. Larger the same sampling decoding. Please zoom for details.
better performances. VCD and ours adopt
capabilities and four subtasks for assessing recognitive abilities in the form of the yes/no question. MMBench systematically evaluates twenty ability dimensions of LVLMs. We present the results of LLaVA-1.5 7B representative in Table 5, SID maintain and improve the multimodal ability on LVLMs benchmarks. In contrast, other CD methods tend to compromise the general multimodal ability. More general benchmark evaluations in Appendix A.5.
Table LVLM benchmark evaluations. DoLa, ICD, VCD, and SID employ the same greedy decoding.
Greedy
Sampling
DoLa
ICD
VCD
OPERA
SID
MME
1510.8+12
1471 S436
1480.7e1a
1473.2+12
1488.5+08
1515.2+1.
1520.4+09
MMbench
64.4
63.9+81
63.7+m
63.0+.2
63.8
64.4
65.0+.23

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Published as a conference paper at ICLR 2025
6 ABLATION ANALYSES
In this section, we conduct ablation analyses on the computation efficiency and hyperparameter sensitivity. More analyses about Token Selection Strategies Analyses, Larger-scale Backbone, Other Decoding Strategies, and Visual Enhancing Decoding Strategy are in Appendix A.7.
analyses
analyses
Methods
Time
Memory
Accuracy
Normal
494
15673
79.11
accuracyt
VCD
904
16753
78.12
WCD
Our
ICD
974
16843
80.21
Sampling
OPERA
2643
21943
79.16
80
80.3
Our8
704
15809
83.11
0.1 a=
a=
0.S
a=l
a.5 9-0.3
f=0.1
8-0.05
A-0
OurS10%
668
15767
83.24
Table 6: Efficiency Comparisons on NVIDIA V100.
Figure 8: Hyperparameter Sensitivity of a and /
10% and 40% mean tokens preserved ratios.
with POPE metric (under the sampling decoding).
Computation Efficiency. One primary concern of hallucination alleviation decoding methods is the computational burden. We evaluate the whole dataset inference time (seconds) and peak GPU mem- ory (MB) on the LLaVA-11.5 7B under the POPE adversarial setting, as shown in Table Contrastive Decoding (CD) methods (Leng et al., 2024; Wang al., 2024a) involve constructing distorted raw inputs, resulting in twice the inference complexity. OPERA (Huang et al., 2024) is based on beam- search decoding and maintains a set of beams to enlarge the candidate range. Additionally, roll curospection-reallooatiio reallocation strategy further exacerbates computational com- plexity. Our SID induces vision-and and-text association hallucinations by pruning large-ratio attention- important tokens in the early layers, which greatly reduces the inference time of CD up to ~30%.
back mechanism in the retrospection
Hyperparameter Sensitivity. Beyond the sensitivity analyses in Fig. 6, we validate the robustness of SID concerning a and / of Eq. 2 and 3, compared to the contrastive decoding methods (i.e., VCD) on LLa 7B. From Fig. (left), is evident that as a decreases, the contrastive de- coding mechanism diminishes. However, SID still achieves pleasant results, while VCD degrades close to Sampling when a=0.1, as the CT strategy induces informative vision-and aatd-aand-exx association hallucinations. When increases, VCD degrades some extent because holistic input disturbance does not always trigger contextual-related hallucination and might exacerbate uncertainty noise. Re- garding B, larger 3 indicates
aggressive truncation of the output vocabulary. Fig. (right) shows that VCD's 's performance heavily relies on large 3 to retain only high-probability tokens. With mild adaptive plausibility constraint (Eq. 3), VCD performs worse than the sample decoding strategy due to output logits influenced by distorted visual inputs. Ours is robust to the 3 setting as the S strategy induces discriminative contrastive logits to generate plausible tokens.
7 CONCLUSION AND FUTURE WORK
In this paper, we firstly re-think contrastive decoding in LVLMMs and empirically find that vision- and-text-agnostic input disturbances in CD do not always amplify desired hallucinations rather than induce potential uncertainty noise. To mitigate these issues, we propose training-free decoding strategy named Self-Introspective Decoding (SID). By developing Context and Text-aware Token Selection (CT*S) strategy, SID amplifies aidion-and-lext association hallucinations guide LVLMs in contrastive decoding, thereby improving faithfulness. Extensive experiments validate the effec- tiveness and robustness of SID.
Future Work: 1) As the pruning ratios and layer are set manually, we consider training the external network to automatically determine optimal hyperparameters, inspired by (Chen et al., 2023b). In addition, to enhance the interpretability of hallucination alleviations, we consider resorting to pre- trained analysis networks to intuitively locate spurious related vision regions. 2) Moreover, given that SID amplifies fine-grained hallucinations, we consider leveraging the CT strategy to auto- matically construct high-quality negative instruction for robust visual instruction tuning rather than relying on expensive G (Liu et al., 2024a; Zhao et al., 2024). Note that the self self-generated hal- lucination dataset ensures style consistency, which is crucial for preference learning (Zhao et al., 2024). Codes available at https: / thui. com http:huuuuuuusttt
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Published as a conference paper at ICLR 2025
8 ACKNOWLEDGEMENTS
This work is supported by two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU15222621, PolyU15225023). We would like to thank Tencent AI Lab for supporting Fushuo Huo as student researcher during his internship.
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Yiyang Zhou, Chenhang Cui, and et al. Analyzing and mitigating object hallucination large vision-language models. In ICLR, 2024.
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A APPENDIX
Overview. More related work are described in Appendix A.1, including backgrounds about Large Vision-Language Models and Decoding Strategy in LLMs. The detailed experimental settings in Appendix A.2 including Model Details and Implementation Details. Evaluation metric details, including CHAIR Evaluations, POPE Evaluations, and GPT-4 Assisted Evaluations introduced in Appendix A.3. We demonstrate the GPT4-V Assisted Evaluation in Appendix A.4 for comprehensive evaluations. Moreover, we illustrate the results on More Benchmarks in- cluding Math Vista (Lu et al., 2024), MMVet (Yu al., 2024c). LLaVA-Bench (Liu al., 2023a), MMMU (Yue et al., 2024a) to evaluate the complex reasoning ability in Appendix A.5. We visualize some Case Study from the LLaVA-Bench-ine Bench-innthe- Wild dataset for qualitative analysis in Appendix A.6. More ablation experiments and analyses including More Analyses on Other Baselines, More Analyses on Adaptive Plausibility Constraint (Eq. 3), Quantitative Attention Scores Analyses, Token Selection Strategies Analyses, Larger-scale LVLM Backbone, Adopting Other Decod- ing Strategies, and Visual Enhancing Decoding Strategy are illustrated in Appendix A.7
A.1 MORE BACKGROUNDS
Large Vision-Language Models. Motivated by the success of Large Language Models (LLMs) (Touvron et al., 2023a; Bai et al., 2023a; Chiang & Li, 2023; Taori et al., 2023; Touvron et al., 2023b; Meta, 2024), recent studies have extended LLMS to multimodal regions and provided Large Vision-Language Models (LVLMs) (Liu et al., 2023a; Zhu et al., 2023; Chen et al., 2023a; Ye et al., 2023: Li et al., 2023a; Bai et al., 2023b; Li et al., 2023c; Dai & al. 2023; Liu al., 2024b; Bavishi et al., 2023; Chen et al., 2024c; Li et al., 2024) powered by pre- pre-trained LLMs. LVLMs understand and generate diverse content in more comprehensive way by integrating user instruction and vision inputs. L.Ia VA (Liu et al., 2023a) connects open-set vision encoder with L.MSs (i.e., Vicuna (Chi- ang & Li, 2023)) by instruction tuning with elaborated language-image Then, LL5 (Liu et al., 2024b) develops the vision-language connector that is data-efficient and powerful for better multimodal understanding. Shikra (Chen et al., 2023a) further incorporates grounding data and trains the model to understand the grounding knowledge in the given images. BLIP-2, InstructBLIP, and MiniGPT-4 (Li et al., 2023c; Dai & et al, 2023; Zhu et al., 2023) in- fusion multimodal features and largely reduce image
nnstruction-following data.
troduce a learnable querying transformer tokens. Fuyu (Bavishi et al., 2023) proposes a vanilla decoder-only architecture without the vision encoder and adapter that makes it easier to understand, scale, and deploy. InternVL (Chen et al., 2024c) proposes three simple but effective improvements, including a strong vision encoder, dy- namic high-resolution, and high-quality bilingual dataset. Recently, built on SOTA open-source LLaMA 3 (Meta, 2024) and increasing the input vision resolution to 4x more pixels, LLaVA-NeXT (Li et al., 2024) exhibits excellent multimodal capabilities. Despite the impressive results, all of the above LVLMs suffer from serious trustworthy issues (Liu et al., 2024f;e), especially the halluci- nation problem, and we mainly conduct experiments on advanced LVLMs, including InstructBLIP, Shikra, LLa and LeaVA-NeXT
Decoding Strategy in LLMs. Selecting decoding strategies in language models is crucial, as it determines how models generate text. Greedy decoding selects the highest probability next token at each step but might lead to less varied text. Beam search (Graves, 2012) is an accumulated- score-based decoding strategy. It maintains set of beams to enlarge the candidate range and finally selects the best one in beams, which is more sophisticated than greedy decoding. Sampling de- coding generates the next words by randomly selecting from the output distribution, where Top-k sampling (Fan et al., 2018) samples from Top-k likely tokens (Fan et al., 2018) and brings diversity but sometimes induces less coherent outputs. Top-p (Nucleus) sampling (Holtzman al.. 2020) improves Top-k sampling that considers the dynamic number of words that reach the probability p. achieving balance between randomness and relevance. As demonstrated while greedy decoding consistently surpasses sampling decoding in terms of hallucination metrics, most open-source and closed-source LVLMs default to using sampling decoding to promote diverse and coherent chat interactions. Therefore, it is practically meaningful to consider the constraints in Eq. 3 when analyzing sampling decoding. Recently, to alleviate the hallucination issue, DoLa (Chuang et al., 2024) decoding emphasizes the knowledge of mature layers and downplays that of pre- pre-mature layers. OPERA (Huang et al., 2024) is established on beam-search decoding strategy and finds the interesting phenomenon of high-probabilityy co-occurrence between the hallucination
Tables and 3,
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and the knowledge aggregation patterns. OPERA penalizes 'Over-Trust Logit' in the beam score to alleviate aggregation patterns. Selfs-enroeeecctvve Decoding (SID) can be seamlessly integrated into different decoding strategies to mitigate hallucinations without sacrificing text generation quality, such as diversity, coherence, and relevance.
A.2 DETAILED EXPERIMENTAL SETTINGS
Models Details. To validate the effectiveness of our SID, we conduct experiments on four repre- sentative LVLMs: InstructBLIP (Dai & et al, 2023), Shikra (Chen et al., 2023a), VA-15- (Liu al., 2024b). and LLaVA-NeXT (Li al., 2024). InstructBLIP employs Q-former (Li et al., 2023c) to condense image tokens to 32, as a result, we are unable to visualize the dynamic token pruning process of InstructBLIP like Fig. 3 and 4. Shikra, LLaVA-1.5. and VA-NeXT directly lever- age linear projection layers as vision- lasoon-anguuae connectors to align multimodal features. Shikra and VLa encode 256 and 576 image tokens to LVLMs. LLa VA-NeXT increases the input vi- sion resolution by 4x to capture more visual details, resulting in 4x more encoded vision tokens than LLaVA-1.5. All LVLMs utilize pre- pre-trained vision encoders like CLIP (Radford et al., 2021) vision encoder, well pre-trained LLMs as language decoders, such as Vicuma v1.1 (Chiang & Li, 2023), LLaMA 2 (Touvron et al., 2023b), and recently released LLaMA (Meta, 2024). We provide results the 7 Billion (B) scale, and larger-scale results are in the Appendix A.7.
Implementation Details. For sampling and greedy decoding, we adopt the default hyperparameter settings. As for Dola (Chuang et al., 2024), it is designed to alleviate hallucinations (i.e., improve factuality) of LLM by contrasting the differences in logits obtained from projecting the later layers versus premature layers. Dola is sensitive to the premature layer selection, we adapt Dola LVLMs. following OPERA (Huang et al., 2024) to utilize "0,2,4,6,8 8,10,11 12,11,,4" as the indexes of candidate premature layers and "32" as the index of the mature layer. The repetition penalty is set to 1.2, as Dola suggests. OPERA, VCD, and ICD are proposed for LVL.Ms and we adopt the default settings. For fair comparisons, SID's hyperparameters of Eq. 2 and follow VCD and ICD. Moreover, we apply SID, VCD, and ICD in both sampling (Top-p=1) and greedy decoding settings for compre- hensive comparisons. Note that due to amplified fine-graines hallucinations, SID more robust to hyperparameters compared to other CD methods (Sec. 5). Experiments are performed on NVIDIA V100/A100 GPUs.
A.3 EVALUATION METRIC DETAILS
CHAIR Evaluations. The Caption Hallucination Assessment with Image Relevance (CHAIR) (Rohrbach et al., 2018) metric is specially designed to assess objection hallucinations in the im- age caption tasks. Concretely, CHAIR quantifies the degree of hallucinations in generated image caption by calculating the proportion of all objects mentioned in the caption that are not present in the ground truth label pool. There are two common variants of CHAIR: CHAIRi (Cz) and CHAIRs (C's), which evaluate the degree of object hallucination in the instance and sentence level, respec- tively. These two metrics are formulated follows:
C₁ (hallucinated objects)
Cs |captions with hallucinated objects}|
[all mentioned objects}|
[all captions]
The smaller the value of Cj and Css, the better the hallucination alleviation performance.
POPE Evaluations. The Polling-based Object Probing Evaluation (POPE) (Li et al., 2023d) was recently developed assess hallucination problems in LVLMs. POPE queries the LVLMs with the template: Is there a <object> in the image? The ratio between queries about existing and no-existing objects is balanced (i.c., 50%-50%) This benchmark consists of three sampling settings: random, popular, and adversarial, each differing in the construction of negative samples. Specially, in the random setting, objects that are not present in the image are selected at random. The popular setting selects missing objects from the high-frequency pool, whereas in the adversarial setting, co-occurring objects that are not present in the image are prioritized. POPE consists of three different datasets, including MSCOCO (Lin et al., 2014), A-OKVQA (Schwenk et al., 2022), and GQA (Hudson & Manning, 2019). POPE involves 500 images from each dataset with six questions each, ultimately yielding 27,000 query-answer pairs. Accuracy and Fl score are chosen as evaluation metrics. The larger the value of Accuracy and Fl score, the better the hallucination alleviation performance.
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Table 7: GPT4-V assisted hallucination evaluations (Huang et al., 2024; Yin et al., 2023). adopt the same sampling decoding strategy. C: correctness; D: detailedness
VCD and ours
L.a
InstructBLIP
Shikra
LLaVA-NeXT
Setting
Cf
D
Ct
D1
CT
D1
Ct
D1
Sampling
5.18
5.79
4.73
5,10
5.03
5.17
5.34
5,67
Ours
5.97
6.01
5.62
5.16
5.78
5.10
6.47
5.85
VCD
5.46
5.63
4.98
5.21
5.31
5.24
5.92
5.47
Ours
6.16
5.94
5.37
5,46
5.61
5.29
6.12
5.78
OPERA
6.16
5.57
5.29
4.86
5.34
4.87
6.11
5.24
Ours
6.15
5.94
5.76
5.42
5.97
5.88
6.63
6.23
hallucinations evaluated by CHAIR and POPE, GPT-4 assisted benchmark (Zhao et al., 2024) utilizes the fine-grained object-level de- scription in the Visual Genome (VG) dataset (Krishna et al., 2017) as ground-truth and relies on the advanced GPT-4 to judge the detailed (such as positional, relational, and attribute) hallucinations and calculate Sentence-level Hallucination Ratio (SHR). With the generated sentences and manu- ally annotated factual information, GPT-4 is prompted to evaluate whether existing hallucinations sentence by sentence. The prompt template provided in Appendix Fig. 11. Following (Zhao al., 2024), we utilize 200 images from the VG dataset and set max new tokens to 512, with the prompt of 'Please describe this image in detail. ' We conduct experiments on sampling decoding strategies and representative LVLMs decoding strategies: VCD (Leng et al., 2024) and OPERA (Huang et al., 2024).
GPT-4 Assisted Evaluations.
Besides object-existence
A.4 GPT4-V ASSISTED EVALUATION.
To further analyze the hallucinations and text quality for open-end generation tasks, following (Huang et al., 2024; Yin et al., 2023), we utilize the strong multi-modal assistant GPT4-V, which simultaneously processes imput from vision and text modalities. We strictly follow (Huang et al., 2024), which utilizes 500 images from the MSCOCO dataset and prompts LVLM: 'alease describe this image in detail.' with the maximum number of 512. To mitigate the impact of the sequential order fed to GPT4-V, we simultaneously compare the generated texts ob- tained from two decoding methods and instruct GPT4-V to judge the correctness and detailedness score on scale of 0-10 based on the input image. The detailed GPT4-V prompt is in Appendix Fig. 12. We set up three representative pairs of comparison experiments: greedy decoding and ours, CD-based VCD (Leng al., 2024) and ours, and OPERA (Huang et al., 2024) and ours. As shown in Table 7, our SID achieves the best results in terms of most metries. Concretely, our method improves correctness by about 15-20% compared to sampling decoding while not compromising the detailedness level. Compared to advanced hallucination mitigation methods VCD and OPERA, SID generates text with obvious more details and better mitigates the hallucination issue. Since the perceptual and reasoning capabilities of GPT4-V are very close to those of humans, the results of the GPT4-V V evaluation reflect, to some extent, the strong performance of the compared methods in terms of mitigating hallucinations and generating text quality from a human perceptual perspective.
A.5 MORE GENERAL BENCHMARKS EVALUATION.
To further validate the general ability of self-introspective decoding strategy, we conduct experi- ments complex reasoning benchmarks, including Math (Lu et al., 2024), MMVet (Yu et al., 2024c), LLaVA-Bench (Liu et al., 2023a). MMMU (Yue et al., 2024a). Math Vista is robust mathematical reasoning evaluation benchmark that includes series of challenging tasks requir- ing detailed deep visual recognition and compositional reasoning skills. Math Vista is composed of 6,141 examples derived from 31 multimodal datasets involving mathematics. We utilize the 'test- mini' subset for testing. MM is a benchmark designed to evaluate large multimodal models on complex multimodal tasks. It outlines six fundamental vision-language capabilities: Recognition, Knowledge, OCR, Spatial Awareness, Language Generation, and Math, and assesses their integra- tion across 16 emerging tasks. MMVet utilizes an LLM--ased evaluator for open-ended outputs, providing consistent scoring across diverse question types and answer styles. This benchmark offers insights into the abilities of various large multimodal model paradigms and models, extending be- yond mere performance ranking. LLaVA-Bench is rigorous evaluation suite aimed at testing the disual-languee alignment and instruction-folloet following capabilities of LVLMs. Here, we utilize the LLaVA- Bench (In-the-Wil Wild) subset, which includes 24 varied images from different domains, such
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Table 8: LVLM General Benchmark Evaluations.
denotes employing greedy decoding strategy.
Greedy
VCD*
OPERA
SID*
Math Vista
Math
27.1
26.3
27.1
27.4
VM
31.1
30.2
31.1
31.2
LLaVA-Bench
63.4
63.6
64.3
68.7
MMMU
32.6
31.0
32.6
33.4
Table Object-level and Attribute-level Hallucination Evaluations. and denote adopting the same sampling and greedy decoding strategies. Experiments are conducted on LLaVA-1.5 7B.
Object-level f
Attribute-level t
Total
Existence
Count
Position
Color
Sampling
171.7
120.8
112.6
151.0
556.1
Dola"
173.2
122.4
115.2
152.8
563.6
VCD*
174.4
124.1
119.4
153.6
571.5
Ours*
180.5
130.7
123.8
154.5
589.5
Greedy
182.3
130.3
126.8
135.7
594.1
Dola*
180.1
127.4
119.3
154.4
581.2
VCD*
179.5
128.1
123.8
155.5
586.9
Ours*
183.9
132.2
127.8
155.9
599.8
memes, paintings. and sketches, with a total of 60 questions. MMMU serves benchmark for assessing multimodal models on tasks that span multiple disciplines, necessitating college-level subject knowledge, and deliberate reasoning. It includes 11,500 questions across 30 subjects and 183 sub-fields. focusing on advanced perception and reasoning in domain knowledge areas. We compare SID with two representative decoding methods (i.c., VCD and OPERA) on LLaVA- 1.5 7B. Table 8 indicates that our SID enhances reason abilities, particularly in the LLaVA-Bench benchmark. However, VCD slightly degrades LVLM's complex reasoning ability, as reflected in Vatta, MMVet, and MMMU benchmarks. OPERA has little gains in discrimination tasks. These results are consistent with Table 5 (MME and MMbench benchmarks), Table and Table 4. We also illustrate experimental results of Existence, Count, Position, and Color subsets of the MME benchmark in Table 9, which evaluate the object-level and attribute-level hallucinations. Without the assistance of auxiliary analysis networks, method achieves +33.4 and +18.0 compared to sam- pling and VCD on MME subsets, validating the effectiveness alleviating object- and attribute-level hallucinations.
A.6 CASE STUDY.
In addition using crafted metrics (CHAIR and POPE), GPT-4//PT4- evaluations, and MME (Fu et al., 2023) and MMBench (Liu et al., 2023b) benchmarks, we qualitatively present sev- eral case studies of SID's hallucination alleviation ability from (Liu et al., 2023a). As illustrated in Appendix Fig. 13, 14, and 15, SID effectively mitigates hal- lucination these challenging scenes by dynamically amplifying vision-and-text association hal- lucinations. Mcanwhile, it preserves the detailness of each image. As we propose training free decoding method that does not rely on auxiliary analysis networks, inherently carries over ex- isting weaknesses of LVL.Ms. Intuitive case studies, as illustrated in Appendix Fig. 13, 14, and 15, reveal that SID still generates some hallucinations, particularly in finer details such as eye color and vehicle identification specifics. These failures may be attributed to the vision encoder's relatively limited visual perception ability. For future work, it is promising to integrate SID with Intern (Chen et al., 2024c), which scales the vision encoder up to 6B, consider leveraging auxiliary analysis networks like Grounding DINO (Liu et al., 2024d) or OWLv2 (Minderer et al., 2023) to mitigate LVLMs' internal weaknesses.
-Wil dataset
A.7 MORE ABLATION ANALYSES
More Analyses Other Baselines.
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Table 10: Comparisons with tuning-based and auxiliary networks-based baselines, All methods adopt the
greedy decoding strategy. LURE and HA-DPO adopt MiniGPT-4 LLaVA-1.5 7B, respectively.
POPET
CHAIRs
CHAIRi
MME
Greedy
78.6
55.4
14.2
805.7
LURE
78.7
55.1
14.0
846.2
+VCD
78.3
55.0
14.3
813.4
+SID
82.5
52.1
13.6
854.9
SID
82.2
51.9
13.2
838.1
Greedy
83.6
49.6
14.4
1510.8
HA-DPO
85.4
44.7
13.6
1522.3
+VCD
83.0
46.1
13.8
1500.6
+SID
86.2
43.8
12.4
1532.7
SID
85.9
44.2
12.2
1525.3
As SID is a training-free alug-and-play decoding strategy that do not requires robust instruction tuning with curated datasets (Lee et al., 2022; Gunjal et al., 2024; Liu et al., 2024a; Zhao et al., 2024; Jiang et al., 2024; et al., 2024b;a;
al., 2024; Yue et al., 2024b) and post-hoc utiliz- ing auxiliary analysis networks (Manakul et al., 2023; Zhou et al., 2024; Yin et al., 2023; Chen et al., 2024b; Wu et al., 2024a; Feng et al., 2024), we do not compare with above two types of methods for fairness. To further validate the robustness of SID, iliary analysis
compare SID with the method (i.c., LURE (Zhou et al., 2024)) and the robust instruction tuning-bacee based method (i.e., HA-DPO (Zhao al., 2024)) and apply the plug-and-play SID to LURE and HA-DPO on POPE, CHAIR, and MME benchmarks, following their official implementations as shown Table 10. Experimental results indicate that SID outperforms LURE and HA-DPO in most cases. Additionally, seamlessly integrating SID with LURE and LRV can significantly enhance performance.
More Analyses on Adaptive Plausibility Constraint.
To further analyze the effectiveness of Eq. 3, we test contrastive decoding-baate meth- ods with and without adaptive plausibility con- straint (Eq. 3) on open-end generation task (CHAIR (Rohrbach comprehensive MME benchmark (Fu et al., 2023). As shown in Table 11, the adaptive plausibility constraint (Eq. 3) greatly affects generated texts, and most metrics underperform vanilla sampling decoding without the adap- tive plausibility constraint (Eq. 3). The pro- posed SID avoids potential uncertainty noise by inducing fine-grained hallucinations, greatly maintaining the performances when ablating
Table 11: Efficacy Analyses of Eq. on LLaVA-1.5 7B. All methods adopt the sampling decoding strategy, and results are the average of three running times with three random seeds.
al., 2018)) and more
CHAIRS
CHAIRi
MME 1
Sampling ICD
51.3
16,8
1471.5
48.7
13.9
1479.7
w/o Eq. 3
52.2
16.6
1448.5
VCD
48.0
14.3
1481.9
w/o Eq. 3
51.9
17.2
1457.1
SID
45.0
11.7
1487.5
w/o Eq.
12.2
1484.6
Eq. 3.
Attention Scores and Token Selection Strategies Analyses.
Quantitative Attention Scores Analyses. Here we quantitatively analyze vision attention score in terms of POPE (Li al., 2023d) and CHAIR (Rohrbach al., 2018) metrics. Concretely, select the top- 10p-100 and least- 100 important vision tokens out of a total of 576 vision tokens of VA-1.5 7B based on attention score based on Eq. Visual and Instruction Disturbance (VD and ID) are also employed as inputs for analyses. Quantitative results in Table 12 illustrate that 100 out of 576 vision tokens with high attention scores greatly maintain original ability, while low attention score tokens reach almost 50% accuracy for the binary classification problem, which indicates attention scores are a good indicator for vision token importance. As for and ID, disturbance in raw input does not obviously harm the LVLMs' diserimination ability, indicated by the POPE metrie. However, VD and ID significantly compromise the open-end generation tasks reflected by the CHAIR metric (LVLMs tend to refuse to ID shown in Fig. 5 and 10).
Token Selection Strategies Analyses. To validate the effectiveness of SID in selecting low at- tention scores to induce vision-and-text association hallucination, further conduct quantitative
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Table 12: Efficacy Analyses Vision Token Attention Scores with POPE metric MSCOCO dataset and CHAIR metric. We select Top-100 and Least-100 important vision tokens out a total 576 vision tokens of LLaVA-1.5 7B, based on Eq. 5 (i=3). VD: Visual Disturbance; ID: Instruction Disturbance.
Random
Adversarial
Setting
CHAIRs
CHAIRi +
Accuracy
F1 Score 1
Accuracy 1
Fl Score
Greedy
88.8
88.6
79.3
80.9
49.6
14.4
+Topi- 1oo
85.6
83.9
77.1
76.3
32.7
75.2
+Least-100
55.3
66.1
54.0
65.3
63.2
38.7
+VD
88.0
87.6
78.9
79.8
36.7
16.9
+ID
88.2
87.7
79.1
80.1
Table 13: Analyses of Different Token Selection Strategies with POPE on MSCOCO dataset and CHAIR
metrics. We select the high importance scores (Eqq 5) of vision tokens (-Top) and random vision tokens (-
Random) for contrastive decoding. Experiments conducted on LLaVA-115 7B.
Random
Adversarial
Setting
CHAIRS 4
CHAIRi +
Accuracy
FI Score t
Accuracy 1
Fl Score 1
Greedy
88.8
88.6
79.3
80.9
49.6
14.4
Ours
89.3
89.5
83.3
82.3
74.2
72.2
-High
87.0
87.3
76.5
79.4
57.9
25.6
-Random
88.4
87.2
80.9
81.5
48.6
13.5
AVISC
88.4
88.1
79.8
80.5
45.3
14.7
Sampling
84.9
83.2
78.7
78.9
16.8
Ours
88.8
88.7
82.6
82.1
45.0
1[.7
AVISC
87.9
87.9
77.5
79.6
46.6
12.5
experiments under different vision token selection strategies with the same preserved vision to- ken number and Layer i=3 as ours. Table 13 shows that vision tokens with high attention scores degrade obviously, as it does not amplify contextual hallucinations rather than retain original impor- tant information. Contrastive decoding does not benefit from subtracting hallucinations amplified by the disturbed inputs rather than suffers from the potential disturbance noise. Selecting random vision tokens brings improvements in the adversarial setting because randomly selected vision to- kens amplify the over-reliance on statistical bias and language priors, similar to Vision CD (Leng et al., 2024) and Instruction CD (Wang et al., 2024a). However, token-level random disturbance also induces uncertainty noise, resulting in the inferior performance in the random setting greedy decoding. Moreover, AVISC (Woo et al., 2024), in contrast to ours, preserves outlier high attention tokens (named 'blind token') and substracts output logits to counteract the overemphasis of 'blind token.' this way, AVISC promotes balanced consideration of all tokens to alleviate hallucinations. However, Table 13 illustrates that Top-100 vision tokens with high attention scores largely main- tain the original performance. 'blind token' tends to have a high probability of target class logits, and contrastive decoding does improve the target class's ' probability while might bring extra noise. Table 13 indicates AVISC still degrades the greedy decoding to some extent, which indicates the attentional vision re-calibration strategy of AVISC induces some annoying noise. Overall, these experiments further validate the rationality of our token selection strategy based on attention sores.
Table 14: Results on Larger-scale Backbones. Sampling decoding is adopted and results average of three running times.
POPE
CHAIR
Methods
Accuracy
F1 Score
Cs
C
LLaVA-1.5
81.60
80.31
49.6
16.1
+VCD
82.67
81.46
46.7
16.4
+OPERA
82.32
81.10
43.3
13.6
+Ours
84.75
83.17
43.5
12.7
InstructBLIP
77.26
79.23
50.8
19.7
+ +VCD
79.77
80.27
47.9
17.6
+OPERA
80.31
80.91
42.5
14.3
+Ours
81.97
82.21
41.7
13.3
Larger-scale LVLM Backbones.
We validate the effectiveness SID in terms of 13B scale backbones on LLaVA-1.5 and Instruct- BLIP architectures. We choose POPE (Li et al., 2023d) and CHAIR (Rohrbach al., 2018) to
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validate the hallucination issues in both discrimination and open-end generation tasks. Table 14 shows that SID remains effective as backbone networks scale up.
MSCOCO(POPE)
90
Origin Ours
Accuracy(
86
85.4
85.3
85.7
84.4
84
82.3
82
81.5
81.1
80.0
80
78 Top-P
Top-K
t=1.5
t=0.8
Figure 9: Results of different decoding strategies.
Adopting Other Decoding Strategies.
Meanwhile, besides direct sampling and greedy decoding, we conduct experiments LLaVA-1.5 7B using the MSCOCO dataset with various decoding strategies, including Top-p sampling (p=0.9), Top-k sampling (k=50), Top-k sampling with varying temperature (k=50, 1=1.5 and 0.8). Figure shows that, regardless of the sampling strategy adopted, the application of SID consistently helps to alleviate hallucinations and improve the overall performance of LVLMs. This consistency highlights the versatility and effectiveness of SID across different sampling strategies.
Visual Enhancing Decoding Strategy.
Although LVLMs can accurately recognize visual elements, LVLMs have difficulty fully inter- preting those elements in the context of the imput cue and effectively linking that recognition to their internal knowledge. We follow Visual Description Grounded Decoding (VDGD) (Ghosh et al., 2024) by first generating a detailed description the image and append- ing it as a prefix to the instruction. The prompt template is adopted from (Ghosh et al., 2024):
<image> I have been given this image to complete the task described as: inst. To help me complete the task, describe the given image in detail. In the case of real-world scenes, please include all foreground and background objects in the description, their properties (like color, shape, etc.), their relations with other objects, their count, and all other components in the image. In case of non-real-worid scenes, like charts, graphs, tables, etc., please describe the table, mention all numbers (if any), mention the written text, and all other details. Experiments are per- formed on hallucination evaluation benchmarks( i.e., POPE and CHAIR) and the general ability benchmark (i.e., MMbench). We re-implement VDGD based on official codes LLLVA-115 7B. Table 15 demonstrates the effectiveness of VDGD (Ghosh et al., 2024) in LVLM's hallucination alleviation and general reasoning ability. However, the grounding visual descriptions, generated by LVLMs themself, enhance the visual perception reasoning capabilities while might inevitably contain hallucinations. Therefore, VDGD is inferior in the POPE (adversial) subset, which prioritizes co-occurring objects which are not present in the image. Meanwhile, VDGD shares somewhat similar motivations in enhancing vision information via Eq. 6 as analyzed. The experiments Table 2 are consistent with the above results, indicating that boosting the vision information effective in mitigating ballucinations but less effective in complex (i.c., adversarial) environments.
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Published as a conference paper at ICLR 2025
Table 15: Comparisons with Visual Enhancing Strategy (VDGD). denotes employing greedy
decoding strategy.
POPE (random)
POPE (adversarial)
CHAIRS
CHAIRi4
MMbench
Greedy
88.8
79.1
49.6
14.4
64.4
OPERA
88.9
79.2
45.2
12.7
64.4
VIGD*
89.0
79.4
46.7
13.7
65,2
SID*
89.3
83.3
44.2
12.2
65.0
Original
Ours
VD
ID
'You are a confused image captien model.
(dynamic pruning important vision
token based on preceding tokens)
Normal (G reedy):: The image captures a beautiful beach scene with man standing on the sand, flying a colorful kite. The late is soartng high sky, adding a vilrant touch the
the beach, possibly taking break from flying the kite to admire the view Ine beach is surtunded by lush green forest, creating serene and picturesque em inoment. The waves es are crashing gently
appears be oying
shore, the overall atmosphere calm peaceful.
The image depicts beautiful beach scene with large body of water in background The beach lined with bushes, providing a and neturd atmosphere. There several people scatlerd acmes the beach, enjoying the sun and the view intain the host seen floaling near the shore, adling to the picturequue scese. The combination of the besch, bushes, and water creates perfect setting for relacation and Jeisure activities
urss
The image features boautiful banch some with large body of water in the background. The water appears to be mix of blue and seen flying in the sxy, adding touch playfuness the scene, Iain several people scnttered acroes the beach, enjoying the view and the outioors, Some of them are closer to the water, while others are further bock on the sand. The comboration of the kite, the people, the water
VD:
white, crsating serane atmosphere.
alivvly and picturesque beach soane.
ID: W this thing? I'm sony, I'm not sure what you are asking. The image shows several persons on the beach.
Figure 10: Instance Illustration of different disturbance results. Examples from MSCOCO (Lin et al., 2014) inferred by VL-155 7B with = 3 and Top-k=50. Hallucinations are marked in red.
GPT-4 Prompt
Please help me judge the comment of this image is hallucination or correct.
I will give you a list of region description of a image. The format is [x1, y1, x2, y2]: region description, where [x1, y1, x2, y2] the bounding box of the region. Highly overlapping bounding boxes may refer to the same object. This is the ground tnuth information of the image, Besides, give you some factual information about the content of the image (which is 100% accurate). Your judgement should base this information. However, this information only descibe the objects in the region image, so it cannot descibe the subjective part of the image, e.g. atmosphere, style, emotion. In that case, you can retum "Cannot judge".
Asso, will give you list of comments of the image for you to judge if it hallucination. Please give judgement one by one along with the reason.
Y should be: Judgement:
should
1. hallucination correct or cannot judge: <reason>
Here are the region descriptions of the image:
(}
Factual Information:
()
Here is the comment yor to judge (hallucination, correct, or cannot judge):
()
Figure 11: Prompts of GPT-4 for evaluations.
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Published as a conference paper at ICLR 2025
GPT4-V Prompt
You are required to score the performance of two Al 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
image or descri incorrectly in terms of the counts, positions, or colors of objects in the image. Please rate the responses of the assistants scale of 1 10, where higher score indicates better performance, according to the following criteria:
existence somothing not present
1: A ccuracy: whether the response is accurate with respect to the image content. Responses with fewer hallucinations should be given higher scores.
2: Detailedness: whether the response rich in necessary details. Note that hallucinated descriptions should not count necessary details.
Please output the scores for each criterion, containing only two values indicating the scores for A ssistant and 2, respectively. The two scores are separated by space. Following the scores, please provide an explanation of your evaluation, avoiding any potential bias and ensuring that the order which responses were presented does not [Assistant 1]
[End of A ssistant 1]
[Assistant 2]
[End of A ssistant 2]
Output format:
A Acuracy: <Scores of the two answers
Reason:
Detailedness: <Scores the two answers>
Reason:
Figure 12: Prompts of GPT4-V for evaluations.
Instruction
(max tokens=512)
Please describe this
image in detail.
The image features dog wearing gown, possibly painted mona lisa painting. The dog is looking the camer with curious expression. displaying its unique attire. The gown appears to burgundy color and complements the dog's outfit/nialle setting of the painting includes pond and rock formation the background. adding arwork. Additionally, there is another small figure, possibly dog or human, the upper-left corner of the picture.
Greedy:
of depth and atmosphere the
OPERA:
The image features painting dog wearing Renaissance-ttle dress, with hood over its head. The dog appears be poodle,
sitting in a chair, giving impression human-like figure. The painting is quite detailed, capturing dog's facial expression and the intricate design the Renaissance dress. The dog's attire and the overall composition of the painting unique and interesting visual experience.
VCD:
and hood, resembling dog. The face painted on her forehead, and she is wearing hat. The painting captures the unique and whimsical blend of human and camine features 'i'nn background. there boat visible, adding sense of depth and context to the scene. The woman's i's attire and the playful
The image painting
wearing Renaissance-styll
has dog's
dog's
forehead
imaginative almosphere.
The painting features a dog wearing Renaissance paint makeup and veil, posing as Mona Lisa-style figure. The dog's adorned mustache and bat. giving unique and artistic appearance. The dog's sitting position also painted resemble human's, adding to the ereative and humorous nature of the painting iolnin the background, there river visible, which complements the overall composition of the painting. The dog's position and the artistic elements create captivating and amasing scene.
Ours:
Figure 13: Case Study from Bench--ecc--ii-tee-Wia on LLaVA-1.5 7B. Hallucinations are
marked in red.
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Published as a conference paper at ICLR 2025
Instruction
(max new tokens=512)
Please describe this image in detail.
The image features group of three animals sitting around dining table, enjoying meal together. The animals include large brown bear, cat, and a rabbit. They are all seated on the grass, with the bear on left side, the cat in the middle, and the rabbit right of the lable. In' table set various food items, including a placed the center of the table, while the bowl and cup are positioned closer to the edge. The seene appears to be whimsical and playful depiction of the animals sharing a meal.
Greedy:
cup. The is
The image depicts whimsical scene of bear, cat, and rabbit sitting around dining table, enjoying meal logether. The bear on the left side of the table, the cal is in the middle, and the rabbit is on the right side. Each animal has a plate of food in front of them.nt The table is set with various utensils such as forks, knives, and spoons, as well as cups and bowls. There are also couple of cakes placed the table, adding to the delightful atmosphere the gathering.nninlf the background, potted plant be seen, adding touch of greenery lo the scene.
OPERA:
VCD: image features whimsical scenc three animals sitting around a dining table, enjoying a meal together. is sitting one side of the tuble, while cat seated on the opposite side, and rabbit is sitting on the für right side of the table, in) nVarious food items ure placed on the lable, including cup, bowl, and sevenal cakes. The cakes are speead across the table, with one near the center, another
bear
rabbit, and the third one near cat. peaceful and inviting atmosphere.
in lush green field, giving the scene
The watercolor illustration features three animals sitting around dining table, onjoying cookies and doughnuts. A large brown bear is on one side of the table, while a gray cat and a yellowish rabbit are on the other side, The cat hus blue eyes and is drinking from blue cup, while rabbit holds pink cup wal table topped plate various desserts, including multiple cookies and doughouts. The scene set in a field surrounded by green grass and trees, giving the whole watercolor natural and whimsical atmosphere.
Ours:
Figure 14: Case Study from LLa LLaaV--Bench-in-the-WWill on LLaVA-1.5 7B. Hallucinations are marked in red.
Instruction
(max new takens:512)
Please describe this image in detail.
Greedy: The image features a man standing on the back of yellow taxd cab, holding blue stirt. He appears to be ironing the shirt while the taxi is in motion. The taxi is driving down a busy street with other vehicles such as truck and a car visible in the soonc: ini urban enviromment. Aditionally, traffic
potted plants scattered around the are, adding some greenery light can be seen in the backgroumd indicating that the taxt driving through intersection.
are
In image, man wearing yallow shirt is standing on back of aellow taxi ironing clothes. a busy city street, surounded by other vehicles such cars and trucss One car is positioned behind the taxi the left side, while another car can be seen further down the street on the right side, A truck is also visthle on the right side of the street. There here are seveml potted plants placed along the street, adding touch of greemery to urben envirvament. One potted plant is located near conter the scene, while two others are posilioned closer the right side of the stroet.
OPERA:
iniingg down
VCD: The image features man ironing clothes on back a yellow SUV taxi. He is standing on clothes iron, which placed top of motal frome mounted on the back of the vehicle. The man Locused his task, and scene quite unusual it takses place on busy city Inlni' here are several other vehicles in the soene, including trick and a car on the left side of the image, and another car the right side, Addtiionally there are two potted plants visible
scene, one near the center and the other on
the lett side.
Ours in the image, man is imning clothes while riding on the back of yelloo SUV taxi, He is standing on portable desk, which strapped to back the vebicle. The taxi is driving down busy city street, surrounded by various vehicles, including a yellow tæxi and truck. InluT be is woaring yollow shirt and shorts and he appears be focused his task. Groen trees somn the beckground, adding a touch of greenery to the busy urban environment, The man's unconventional choice transportation adds a unique element to busting cityscape.
Figure 15: Case Study from LLaVA Benh---enc--ii-he--Wii on LLaVA-1.5 7B. Hallucinations
marked in red.
24


Reviews:
[{"review_id": "KoYWtYbTyK", "reviewer": "Reviewer_LdUB", "summary": "This manuscript proposes to solve the hallucination issue in Large Vision-Language Models (LVLMs). The proposed method named Self-Introspective Decoding (SID) aims to solve the issue with a different decoding strategy compared to existing ones. The Context and Text-aware Token Selection (CT2S) strategy within SID preserves only unimportant vision tokens after the early layers of LVLMs, in order to amplify text-informed hallucinations during the auto-regressive decoding process and guide the LVLMs to produce more accurate outputs. Evaluation was conducted using four representative LVLMs: InstructBLIP, Shikra, LLaVA-1.5, and LLaVA-NeXT. Evaluation metrics include CHAIR, POPE, GPT-4 Assisted Evaluations and MME and MMBench Evaluations. Performance of the proposed SID was compared with Sampling, Greedy, Dola, and LVLM decoding strategies (VCD, ICD, and OPERA). Experiment results demonstrate the effectiveness of SID to generates less-hallucination and higher-quality texts, with lower additional computation cost.", "questions": "Major comments:\n1. Table 1 is not explained clear enough, according to the results whatever methods used, greedy decoding setting always performed better, than what is the significance of considering the constraints in Equ 3?\n2. What about the possibility of integrating CT2S with other hallucination alleviation strategies?\n\nMinor comments:\n1. Better to keep names of the metrics consistent, e.g., CHAIRI and CHAIRS might be written as CHAIRi and CHAIRs, to be the same as Table 8-9.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed SID with its key component: Context and Text-aware Token Selection (CT2S) strategy is a Training-Free decoding strategy which efficiently and effectively solves hallucination problem in LVLMs without additional costs.\n2. Extensive experiments were conducted and several evaluation metrics were applied to verify the effectiveness of the SID in various aspects and the superiority over existing methods.\n3. Hyperparameter sensitivity evaluation showed the proposed SID’s robustness to different hyperparameter settings.", "weaknesses": "1. The proposed SID is performed on pre-trained LVLMs, so it’s possible that the performance of SID is limited by those pre-trained models.\n2. And currently there’s no solution to specifically tune SID for different LVLMs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6IxOxPnGvA", "reviewer": "Reviewer_Xv5a", "summary": "The paper introduces Self-Introspective Decoding (SID) to alleviate hallucinations in large vision language models. Other available methods either introduce noise or lead to double inference costs. SID offers an alternative using a Context and Text-aware Token Selection (CT2S) strategy that selectively attenuates less important vision tokens in early decoder layers. This reduces irrelevant hallucinations in the generations. The approach requires minimal additional computational resources. The paper presents multiple empirical results to show that the method reduces hallucinations while preserving text quality.", "questions": "1. How does the performance of the proposed approach vary across different LVLM sizes? Do we have any results for larger and smalled LVLMs? (other than 7B)\n2. Where does the proposed approach fail to mitigate hallucinations?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The proposed approach does not require additional computational resources\n- GPT-4 assisted analyses were done to calculate Sentence-level Hallucination Ratio\n- The paper is well written", "weaknesses": "- The paper does not present the results on different benchmarks to show the preservation of LVLM ability. LVLMs should be extensively tested on a variety of benchmarks that test different skills, like - MathVision and Mathvista for mathematical reasoning, MMMU for college-level knowledge on various subjects, MM-Vet/v2\n- The paper does not cover a comprehensive set of baselines - Woodpecker (https://arxiv.org/abs/2310.16045), LRV (https://www.researchgate.net/publication/375596083), LURE (https://arxiv.org/pdf/2310.00754)\n- \"Hallucination, defined as the generation of irrelevant, factually incorrect, or meaningless text in a given context.\" The approach aims to tackle hallucinations in LVLMs as a whole but does not mention or tackle style hallucinations or biases introduced when LVLMs are instruction-tuned.\n\n-Minor:\n(typo) Figure 2 - Gnerated\n\n#256 validate", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VDLHeC4X6S", "reviewer": "Reviewer_d52A", "summary": "This paper proposes Self-Introspective Decoding (SID) for mitigating hallucinations in Large Vision-Language Models (LVLMs). The authors specifically observe that LVLMs are capable of introspectively assessing the importance of vision tokens based on preceding tokens. Based on this, the authors propose a Context and Text-aware Token Selection ($\\text{CT}^2\\text{S}$) strategy to amplify fine-grained hallucinations, which are ultimately mitigated using a contrastive decoding method. Comprehensive experimental results across various benchmarks demonstrate that the proposed SID outperforms other contrastive decoding baselines in alleviating hallucinations in LVLMs, while also achieving lower computational cost.", "questions": "1. In Table 1, the authors demonstrate that removing the adaptive plausibility constraint resulted in lower performance degradation for the proposed method on the POPE benchmark. Does this also hold for the open-ended CHAIR benchmark and the more comprehensive MME benchmark?\n2. How do you define vision-and-text association hallucination?\n3. What is the specific experimental setup in Figure 5? Why does ID produce entirely inconsistent responses?\n4. How do the proposed method and other baselines perform on the Existence, Count, Position, and Color subsets of the MME benchmark?\n5. The experiment in Lines 346-362 is interesting. However, the rationale behind why adding underperforms the proposed method is unclear. From my understanding, boosting the target logits of the original prediction could also enhance discrimination.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed method seems novel and interesting. The motivation is also clear.\n2. Through empirical experiments, the authors highlight that current vision-and-text agnostic contrastive decoding methods can introduce uncertainty noise and degrade performance. This provides valuable insights for future research.\n3. The experimental results are comprehensive and promising, effectively validating the effectiveness of the proposed approach. Efficiency comparisons further highlight the proposed method’s advantage over other contrastive decoding approaches.\n4. The authors provide the code implementation for this work, enhancing its reproducibility (though I have not run the code myself).\n\n\n\n\n\n\n\n5. The writing and presentation of this paper is good.", "weaknesses": "1. The proposed method shares some similarities with AVISC [1], which also identifies less-important visual tokens for contrastive decoding. A detailed discussion of these similarities, along with a comparison of experimental results, would strengthen this paper.\n2. In Figure 6, pruning all vision tokens results in less than a 0.5% performance difference from the optimal setting, suggesting that the proposed strategy of selecting the top-k least important tokens provides only marginal performance gains. Additionally, could you clarify the statement, “*The loss of vision information for subsequent decoder layers results in losing the visual context, leading to aimless hallucinations without sufficient vision grounding*”? Are there any examples or experiments that illustrate this?\n3. The proposed method achieves marginal performance improvements on the MME and MMBench benchmarks. Could you please include the standard deviations for the two benchmarks, as their performance is sensitive to random seeds?\n4. The proposed method may lack interpretability, as the selected less important tokens (shown in Figures 3 and 4) do not carry true semantic meaning, unlike other contrastive decoding approaches such as HALC [2]. Besides, the proposed method seems not very reasonable when applied to simple LVLMs such as InstructBLIP, which only has 32 vision tokens. In this case, preserving only the least important tokens provides a very coarse measure of vision importance and is unlikely to effectively induce vision-and-text association hallucinations.\n\nMinor issues:\n1. In Table 6, is the inference time calculated per instance or for the entire benchmark?\n2. Wrong citation format in Line 910.\n3. In Table 1, why are there no standard deviations reported for the VCD method in the Sampling setting? Additionally, are the three experiments conducted using three different random seeds? The reported deviations are lower than expected.\n\n[1] Woo, S., Kim, D., Jang, J., Choi, Y., & Kim, C. (2024). Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models. *arXiv preprint arXiv:2405.17820*.\n\n[2] Chen, Z., Zhao, Z., Luo, H., Yao, H., Li, B., & Zhou, J (2024). HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding. In *International Conference on Machine Learning*.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CSrZMqqxu5", "reviewer": "Reviewer_yTiw", "summary": "1) The main contribution os the paper is proposing that some vision tokens with low attention scores can induce hallucinations while decoding. \n2) The paper backs this up by conducting experiments to prove that low score vision tokens focus mainly on unrelated regions in the image. \n3) The paper does extensive ablations on till which layer to prune vision tokens and comparison with other baselines.", "questions": "Refer to weakness section.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) Presentation of the paper is clear and straightforward.\n2) The paper does a good set of experiments to support their argument that low importance score tokens induce hallucinations.\n3) The paper does sufficient ablations and evaluations to prove its approach.", "weaknesses": "1) An important baseline the paper has missed is a simple but effective one, how would the comparison look if we append the image description before asking a question? Ask the LVLM to describe the image and append it to the question and pass through the model again and see if it has any improvement. Some research on this includes - Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs (Ghosh et al, 2024).\n\n2) A few more important benchmark comparisons are needed - MathVista, MMVet, LLaVA-Bench, MMMU", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)