Unveiling the Tapestry of Consistency in Large Vision-Language Models
Authors: Yuan Zhang
Fei Xiao
Tao Huang
Chun-Kai Fan
Hongyuan Dong
Jiawen Li
Jiacong Wang
Kuan Cheng
Shanghang Zhang
Haoyuan Guo
Venue: NeurIPS 2024 poster (2024)
Keywords: Consistency
ConBench
Large Vision-Language Models
Analysis
Primary area: evaluation
TL;DR: We propose a Consistency benchmark, get an in-depth analysis and design a simple method to improve VLMs.

Abstract: Large vision-language models (LVLMs) have recently achieved rapid progress, exhibiting great perception and reasoning abilities concerning visual information. However, when faced with prompts in different sizes of solution spaces, LVLMs fail to always give consistent answers regarding the same knowledge point. This inconsistency of answers between different solution spaces is prevalent in LVLMs and erodes trust. To this end, we provide a multi-modal benchmark ConBench, to intuitively analyze how LVLMs perform when the solution space of a prompt revolves around a knowledge point. Based on the ConBench tool, we are the first to reveal the tapestry and get the following findings: (1) In the discriminate realm, the larger the solution space of the prompt, the lower the accuracy of the answers. 
(2) Establish the relationship between the discriminative and generative realms: the accuracy of the discriminative question type exhibits a strong positive correlation with its Consistency with the caption. (3) Compared to open-source models, closed-source models exhibit a pronounced bias advantage in terms of Consistency. Eventually, we ameliorate the consistency of LVLMs by trigger-based diagnostic refinement, indirectly improving the performance of their caption. We hope this paper will accelerate the research community in better evaluating their models and encourage future advancements in the consistency domain.

Unveiling the Tapestry of Consistency in Large Vision-Language Models
Yuan Zhang! Fei Xiao?, Tao Huang?, Chun-Kai Fan', Hongyuan Dong", Jiawen Li Jiacong Wang Kuan Cheng', Shanghang Zhang'; Haoyuan Guo
School of Computer Science, Peking University ByteDance Inc The University of Sydney School Artificial Intelligence, UCAS https:/ thub. com/ Foundation onniiion-mmmeme
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Abstract
Large vision- Lasionlaaguuage models (LVLMs) have recently achieved rapid progress, exhibiting great perception and reasoning abilities concerning visual information. However, when faced with prompts in different sizes of solution spaces, LVLMs fail to always give consistent answers regarding the same knowledge point. This inconsistency answers between different solution spaces is prevalent in LVLMs and erodes trust. To this end, we provide multi-modal benchmark ConBench, to intuitively analyze how LVLMS perform when the solution space of a prompt revolves around a knowledge point. Based on the ConBench tool, we are the first to reveal the tapestry and get the following findings: (1) the discriminate realm, the larger the solution space of the prompt. the lower the accuracy of the answers. (2) Establish the relationship between the discriminative and generative realms: the accuracy of the discrininative question type exhibits strong positive correlation with its Consistency with the caption. (3) Compared to open-source models, closed-source models exhibit pronounced bias advantage in terms of Consistency. Eventually, we ameliorate the consistency of LVLMs by trigger-based diagnostic refinement, indirectly improving the performance of their caption. We hope this paper will accelerate the research community in better evaluating their models and encourage future advancements in the consistency domain.
1 Introduction
Recently, benefiting from notable advancements in large language models (LLMs) [1; 25; 2}, the realm large vision-language models (LVLMs) has undergone revolutionary transformation. These novel LVLMs [18; 24; 3; 8; 15; 13] try combine visual signals with textual semantics and spark cognitive brilliance across modalities. Although LVLMS can generate high-quality re- sponses to task prompts, we discover that for correctly answered cases, simply modifying the prompt will result LVLMs in providing contradictory responses. Figure (a.2), LLaVA-7B [18] properly describes the picture "It is when prompted "Is the dinosaur played by humans? Please answer yes or no. " it re- sponds with "No, they are dinosaurs". The above phenomenon of Inconsistency is widely observed across mainstream LVLMs, and a preliminary study was conducted only on LLMs [14]. In practice, in contrast to the fixed patterns of questions, designed in existing multimodal benchmarks, the users tend to pose questions in arbitrary ways. Therefore, it is necessary to ensure the LVLMs in predicting correct and consistent answers, even when faced with various formats of queries.
wearing dinosaur costume ". but
"Correspondence to: Shanghang Zhang and Haoyuan Guo. 1 State Key Laboratory of Multimedia Information Processing.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Top1 Qwen- DivenVL-Maxx
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Top2
Top3 InteraVL-w1..PP400
Inconsistency Cases
Caption: Describe this and details count.
Solution spact
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ConBench
TraefFalse the dinosaur
played by humaus? Please you
Analysis
Accuracy
No, thiy dinosauers
Closedisounce
Caption: Deseribe this image and details.
Open-sounce
The picture shows man wearing dinasaur costume...
TraeFaalv
Cholces
Accuncy
(a) Inconsistency Cases
(b) ConBench
(c) Analysis
Figure 1: Here is the overview of paper. Part (a) indicates two examples of Inconsistency between discriminative answers and generative captions, where the answers marked in blue contradict the answers marked in purple. Part (b) shows the Consistency evaluation method Conbench and its discriminative top three leaderboard. Part(c) reveals the main three findings built upon ConBench.
However, there are currently no benchmarks or research studies that specifically focus on evaluating the Consistency of LVLMs responses. These single-prompt type evaluation approaches [12: 10; 28; 21; 6] lead a disconnect between benchmark accuracy and real-world user practical experience.
Based on the above observations, we systematically introduce Consistency Benchmark dubbed ConBench, to estimate the capabilities of LVLMs more thoroughly via diverse question formats. consists of 1, 000 public pictures, and each was manually selected from four multimodal benchmarks [10; 12; 28; 21]. Apart from the original discriminative prompt, we constructed two additional discriminative types of questions' by ChatGPT/GPT-4 [1]. Notably, three types of questions of each case are around the same knowledge point. Besides, every set is accompanied by generative question without ground truth. Consequently, ConBench serves as an evaluation tool that observes the Consistency performance of LVV.Ms and surpasses the limitations of previous assessments.
Furthermore, grounded on the ConBench, we conduct an in-depth analysis and visualization of Consistency on 14 popular LVLMs. In nutshell, the conclusions noteworthy insight are threefold:
C1 the discriminative question-answering (QA) domain: (1) A decrease LVLMs accuracy as the prompt's 's solution space increases. (2) Instances of erroneous yet consistent answers scarce.
C2 Extended the generative domain, we establish connection between discriminative and generative domains by the perspective of Consistency. (1) As the solution space of discriminative questions expands, the Consistency between its answer and caption grows stronger. (2) The accuracy of discriminative answer and its Consistency with the caption exhibit positive correlation.
C3 Closed-source models exhibit pronounced bias advantage in terms of Consistency, compared open-source models. This provides an alternative perspective to demonstrate why closed-source mod- els, despite sometimes having lower accuracy, offer better user experience in practical applications.
Eventually, leveraging the insights gained from our theoretical discoveries, we enhance the caption performance of LVLMs without any additional costs associated with training. Specifically, construct discriminative prompts based on the low-confidence words in the answers of LVLMs, forcing the LVLMMs introspect. Then, through iterative refinement in multiple rounds of question- answering, the quality of LVL.M' captions gets an impressive achievement (e.g., our method improves the LLAVA-Ne [19] by 9.1% and MiniGemini-34B [15] by 9.6% metric[C] in Sec. 3.4).
In summary, to the best of our knowledge, we are the first to propose a Consistency evaluation method and conduct comprehensive analysis of Inconsistency in LVLMs. We hope this paper serves catalyst for further exploration, and look forward the community applying the above findings to polish up the usability and practicality of large vision-languee models.
e.gg. Multiple-choice questions and limited VQA questions generated for MME benchmark.

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2
Related Work
Large Vison Language Models With the impressive success of large language models (LLMs) [1; 25; 2; 4; 291, recent studies work on generative large vision-language models (LVLMs) [18; 24; 8; 15; 27; 30] to improve multimodal comprehension and generation through utilizing the strong generality of LLMs. Built upon the CLIP [23] image encoder which is somewhat aligned with the language modality, current LVLMs typically utilize vast image-text pairs to connect the vision encoder and LLM, enabling LLM receive and understand visual content. For instance, LLa [20] directly connects the vision encoder and LLM with MLPs, showing proficiency in multi-modal dialogues. Subsequent works have further enhanced LVLMs by improving the multi-modal instruction data [18; 27; 5] and designing novel modules [3; 4; 26] for more sufficient modality alignment.
Conventional Multimodal Evaluation A multitude of public multimodal benchmarks, such as MME [10], SeedBench [12], and MMBench [21], further advance objective evaluation of LVLMs by only constructing True/False questions or multiple-choice questions, where the absence of diverse question types causes instability. In addition, their objective metrics solely emphasize the LVLM's accuracy, disregarding its robustness and security. The above issues can lead situation where some LVLMs have lower accuracy in evaluation results but provide a better user experience, To systematically assess the comprehensive capability of LVLMs, we propose a simple and efficient evaluation approach that relies on checking the Consistency between different kinds of prompts.
amount of prior work has been conducted on investigating Inconsistency in LLMs. [14] is the first to find the Inconsistency phenomenon in question- question-answeringg answering and validator tasks and define GV-consistency. Besides, it leverages consistency pair for training to improve LLMs' performance. While [17] utilizes Consistency to check for hallucination detection in LLMs, logic consistency-base based method that involves logic-related questions and answers. Compared to LLMs, Inconsistency in LVLMs is more likely to occur due to the additional visual modality, which deserves further exploration.
Inconsistency in LLMs
30
3 ConBench
60
30
Antent
140
Cal
We propose a novel multimodal evaluation pipeline named ConBench to comprehensively assess LVLMs. The ConBench has a total of 4K questions on 1K images and corresponding 3K discriminative ground
Some
20
Physis
Poste
Attribute
chemitry
ConBench
Recognition
truths, guaranteeing evaluation quality in terms of the quantity and diversity of questions. In Sec. 1, we present the generation of ConBench and the con- struction pipeline for prompts. Sec. 3.2 introduces the hierarchical core capabilities and discusses the design philosophy. Sec. 3.3 and 3.4 describe the eval- uation strategy for scoring various types of answers.
Colebbrity
OCR
20
Attibota
Rezsoring
mestimai rich
Pouitin
Calcutation
Kaowledge2451
120
Trem
Cone
120
Cagnitiennaaaa
80
20
Sensationtt8S
20
2
Laui D0yttaly
Figure 2: Overview of 19 evaluation de- tailed categories in ConBench.
3.1 Data Generation Process
Image Filter We manually chose 1K images from four high-quality multimodal benchmarks: MME
[10], SeedBench [12], MMBench [21], and MMMU [28]. MME is a true/false question type, while SeedBench and MMBench cover comprehensive multiple-choice questions. Meanwhile, MMMU emphasizes the knowledge level. The criteria for the image filter include: (1) resolution is more than 224 224 (2) the image rarely occurs in the mainstream training dataset (c.g., COCO [16] and Cityscapes [9]) (3) There are more than 3 foreground objects in the image. The above criteria ensure the quality of content in images.
Prompt Construction Each image is accompanied by its original discriminative prompt, and we constructed two extra discriminative questions. Therefore, case owns three discriminative prompts (true/false, multiple-choice and limited VQA questions) with a generative caption prompt around the same knowledge point. Firstly, we modified the original prompts whose answers can be directly inferred from the text instead of the image, to force LVLMS to utilize information from the visual features. Next, we employed GPT/GPT-4 to generate the extra discriminative types of questions,
3

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which were then subjected to the manual review, and the proposed prompt is listed in Figure 3. Finally, avoid bias the LVLMs that may affect the evaluation results, the true/false questions have a 50% distribution for both correct and wrong ground truths. For the multiple-choice questions, each option (e.g., A, B, C, D) has an equal probability distribution of 25% for being the correct answer. Notably, to ensure an accurate evaluation parser, limited VQA questions are subject to certain restrictions, like specifying the word count and answer format (e.g., fractions/ abbreviations / numbers).
3.2 Hierarchical Core Capabilities
The ConBench comprises three core capabilities, arranged in ascending order of difficulty, namely: Sensation, Cognition, and Knowledge, with nineteen fine-grained dimensions shown in Figure 2. [Easy Mode] Sensation: What you see is what you get. We assume that sensation is the most fun- damental expertise of LVLMs, and it is the "eye" of the LLMs. While perceived questions appear simple and basic, they are nonetheless essential. Therefore, this capability accounts for 50% of the ConBench. Count, color, optical character recog- nition (OCR) and scene categories focus on subtle details, while poster, attribute recognition and po- sition types emphasize the overall picture.
"Prompt Construction" Prompt:
You are question expert. Give you a [Disctiminative type] question, and you should generate two other kinds questions. The [Diseriminative rype] question Based on the [Diccriminative type] question above, a [The other discriminative type] about the [Category] with following answer, and a VQA question about [Category] with following answer are genetated for the same knowledge point.
[Original Prompe].
Figure 3:
The prompt for generation of dis- criminative questions. Please zoom in to view.
[Medium Mode] Cognition: Go beyond the surface. The cognitive process needs the model to integrate visual and language modalities: observing the content of image, combining it with the text of question, and retrieving knowledge from within the LLMs. It is more challenging than the single sensation task. This section constitutes 26% of the ConBench, including numerical calculation, code inference, text translation, math, cross-instance reasoning and attribute reasoning categories.
[Hard Mode] Knowledge: Master the art of synthesis and integration. Mastering professional knowledge is an essential pathway for next-generation LVLMs to become Expert AGI, as requires a higher level of understanding of images and the application of expert knowledge. We carefully selected knowledge from diverse and extensive fields, such as celebrities, chemistry, physies, biology, art and geography. This part takes up 24% of the total, and functions the upper limit of ConBench.
3.3 Results Parser
For true/false questions, we first extract the "yes" and "no" from the answer. If both of them absent, the answer would be considered as "none". Then, we strictly compare the extracted answer with the ground truth. If they match exactly, the true/false response is considered correct.
When parsing the outcome of multiple choices, we derive the choice label (e.g., A, B, C, D) from If successful, utilize this as the prediction and match the ground truth. If not, we will not proceed with further extracting the answers. Because each prompt of choices, we specified that only one letter needs to be answered. Doing so would be unfair to LVLMs that excel in following instructions.
We still utilize character matching for the answer of limited VQA instead of GPTs. On one hand, we have taken strict formatting constraints on the prompts. For instance, in physics and math, there are restrictions on answering with fractions (c.g.. 1/2), while in geography at the city level. On the other hand, the cost of the GPT's judgment is high and the waiting time is delayed. Specifically, the parser based on the Average Normalized Levenshtein Similarity (ANLS) [22], where the threshold is set to 0.95 and M N = 1. When parsed result s > 0.4, we consider the answer to be exactly right.
3.4 Multidimensional Evaluation Metric
Here we provide two evaluation metrics, each from the perspective of discriminative and generative domains, aiming to provide a more comprehensive understanding of LVLMS consistency. The former does not rely on Al tools and quickly produces Consistency results among discriminative responses via Sec. 3.3, primarily evaluating the knowledge. The latter employs GPT to indirectly assess the quality of captions, by judging the consistency between discriminative responses and captions.

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Consistency
Q: Captlon
A: Caption
Paragraph
Generative Answers
Tromlialse
Q/A
Inconsistency
True / False
Machine Reading Comprehension (MRC) Task
A: Mulliple Choices
you teaching expert. will provide you with passage. a questton, and an answer filled in by students.
passag: [Paragraph].
A: VQA
queations: [Q]
students answers: [A]
LVLMs
Please hilp fairly deteranine whether the student answer
Discriminative
with answer you provided. Only outpul yes or no.
Figure 4: The pipeline of judging Consistency between caption and discriminative answers via GPT/GPT4. Please zoom in view the prompt.
Table 1: Evaluation[D] of mainstreams series of LVLMs ConBench. The detailed results of the Sensation. Cognition, and Knowledge core capabilities are listed below. C, and V represent true-false, multiple-choice, and limited VQA questions, respectively. The ranking can found below the respective numbers. f:
Dae safety considerations, GPT-4V declined to answer the celebrity category.
Sensation
Cognition
Knowledge
Method
ConScore[D]
T
V
Con
C
V
Con
T
C
Con
Closed-source Vision Longuage Models
GPT-4V1 [i]
29.20s
80.4
79.0
61.7
48.3
68.8
53.2
39.9
20.4
63.1
57.2
30.0
14.2
GPT-4-Omai [1]
35.70g
89.2
79.1
6144
55.0
71.8
62.8
11.9
27.8
61.7
61.7
39.7
23.3
Gemini Pro Vision [24]
25.00
85.2
60.7
63.4
39.3
71.8
45.0
44.2
15.1
65.0
51.4
39.7
15.8
Gemini-Ulra Cltro-Vision [24]
33. 10,
78.9
00.3
58.5
47.9
28.5
62.9
62.2
44.7
19.7
Qwen Qwen-VL.PPlar
28.107
82.7
60.4
41.7
30.8
16.3
63.6
33.3
Qwen- VL-MMx [3]
37.00,
86.4
80.7
65.4
51.4
51.3
08.3
58.6
38.9
78
Vision Longuage Models
LLaVA-v1.5-7B [18]
16.60
79.3
56.8
11.3
28.3
51.4
33.5
15.8
4.7
61.7
11.4
16.9
7.8
Owen Qwen.VL. Vl. [3]
26.40g
81.0
79.6
54.2
39.0
55.0
46.3
33.2
13.5
60.3
54.2
28.9
14.7
13B
Vision Language Models
LLaVA-v1.5-13B [18]
21.00;1
82.9
77.1
19.6 6
39.5
53.6
37 8
20.1
10.4
65.6
50.3
17.2
9.7
MiniGemini- 13B [15]
21.801a
81.9
69.7
52.8
39.3
51.9
38.2
21.1
6.9
52.8
36.7
17.5
9.2
IntemVL..1..5-68
31.40s
85.6
65.0
54.3
59.7
58.6
44.4
19.4
58.1
25.3
12.2
~ 34B Vision Langange Models
LLaVA-NeXT:34B [19]
27.70s
82.4
55.6
60.4
MiniGemini-34B [15]
23.001a
80.8
76.8
48.2
39.7
36.9
30.7
18.9
6.0
58.1
42.3
20.8
IntemVL-v1.2P-00B [8]
31.70s
83.7
83.2
66.6
53.4
74.2
67.6
57.1
34.9
72.2
58.3
28.6
13.6
Discriminative Domain Evaluation Metric We define the ConScore[D] as that: when all three discriminative types of questions within the same case are answered correctly, the model gets one point. The maximum score 1000 points. The final format is presented as a percentage (%).
Generative Domain Evaluation Metric Due to the high variability in captions, it is not possible rely GPT/GPT4 for judgment. The judging process and the constructed prompts are shown in Figure 4. We formulate as machine reading comprehension task. We manually sample the judgment results, and GPT4 achieved an accuracy rate of 95%. which is reliable and trustworthy. Next, we define the ConScore[C] as the average score of Consistency between the caption and the other three discriminative responses.
calculate Consistency based on character matching alone. Therefore,
4 Analysis
4.1
Evaluation Results
In this section, closed-source and open-source representative LVLMs with varying sizes and architectures are evaluated our Consistency benchmark, including GPT-VV [1], GPT4-Omni [1], Gemini-Vinn Vision [24], Qwen-VL. series [3], LLaVA series [18; 19]. MiniGemini series [15] and InternLL series [8]. The evaluation results ConBench are listed in Table and 2. In the metric[D]
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Table 2: Evaluation of Consistency between caption and three discriminative types of answer on ConBench. The "rank diff" means the difference between ConScore[D] and Score[C]. The Con[X] is the Consistency ratio between discriminative answer type X and caption. The "ordered" represents whether Con[T] Con[C] Con[V] is in its line.
Method
Rank Diff ConScore(C] Con[T]
Con[C]
Con[V]
Ordered
Closed-souee source Vision Language Models
[1]
55.6g
59.10
GPT-4 Omni [1]
2
58.00
62.50
66.10
Gemini Pro Disiii [24]
48.4g
43.30
45.20
56.80
Y
Gemini- Ultra- Vision [24]
54.6g
47.80
55.20
60.70
VL-Plus 131
50.2,
47.10
54.30
Qwen-VL-Max, VL-Max [3]
Qwen-
58.4g
54.30
58.00
62.90
7B Vision Langrage Models
LLaVA-v1.5-77 [18]
38.444
39.20
36.60
39.50
Twen-VL-Chat133 [3]
48.0.1
51.30
13B
Vision Langaage Models
LLaVA-vI.5-138
44.412
41.50
45.80
46.00
Y
MiniGemini-IßB [15]
4.1.1s
38.80
42.90
43.30
IntemVI.v1.5-26B [7]
50.9g
44.50
54.20
Vision Language Models
LLaVA-NeXT-34B [19]
2
48.310
46.00
52.20
46.80
MiniGemini-34B [15]
1.4
49.6g
56.80
48.00
44.10
IntemVV1-v1.2P-008 [8]
4.2
53.7g
55.80
1, OW-N-VLLMMx [3] secures the top position, leading the second-place GPT4-Omni [1] by a margin of 1.3%. The InternVL-v' 1.2P-40BB [8] performs best in the open-sourced community, especially in cognition capability. The LLaVA series did not make it to the top ten. In the metric[C], the newest GPT4-Omni [1] leads the leaderboard, which is the only model that surpasses 60. It has significant advantage over the second-place model Qwen-VL-Mar VL-Max [3], with a gap of 3.8. We observed that
although the GPT series slightly underperforms Qwen-Max in metric[D], it significantly outperforms the Qwen series in metric[]], which aligns with our actual user experience. Actually, ConScore[C] provides an alternative quality description of captions, because higher recall and precision rates usually match better Consistency. Besides, rankings of LVLMs show a slight variation between metric[C] and metrie[D]. The GPT series models claim better performance of caption generation.
4.2 Discriminative Domain
To investigate what causes the Inconsis- tency between different types of prompts,
120
we first conduct analyses on the discrimi- native domain to compare the performance differences. We summarize our findings
0.9859
0.50
0.98
0.93
112 82
LIVA
110
Confiidnce of answers
107.43
Loglts of answers
03
100
into the following facts:
0:76
95.89
0.71
Fact 4.2.1 (Inconsistency in Accuracy). The accuracy of the answer decreases as the solution space of the discriminative prompt increases.
0.7
0.55
90
360
85.02
0.6
0.5
72.01
As shown in the columns of "T", "C", and "V" in Table 1, the accuracy decreases solution space expands in all core capa- bilities. For instance (e.g., the Sensation of GPT-4-Omni), the double- cuuble-choice true-false questions achieve an accuracy of 89.2, whereas the accu- racy for multiple-choic and VQA questions on the same case declines to 79.4 and 61.7. respectively. This is understandable, as the number of potential choices increases, the difficulty identifying the correct answer also rises.
70
Trun/Pase
Choice
VQA
True/Faler
Choice
VQA
Figure 5: The confidence and logits of answers of LLaVA-13B and MGM-13B.
Fact 4.2.2 (Inconsistency in Wrong Answers). Cases of erroneous yet consistent answers scarce.

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Table 3:
The Consistency between multiple choices and VQAs, including both correct and wrong, Each case picked up in order from top to bottom from the LVLMs in Table 1.
Con(Correct] (%)
35.00
39,90
31.20
34.10
41.60
37.00
39.40
29.30
39.70
25.90
28.70
22.20
Con[Wrong] (%)
0.30
0.40
0.20
0.50
0.40
0.50
0.30
0.40
0.20
0.50
0.10
0.20
Analysis of (True/False) with Caption
Analysis of (Choices) Caption
Analysis (VQA) with Caption
Cormenecy fain Dariiiia derovve and Exptiont9g
GPr 40
GPT,4 1.
Owiin, Chet
65
OWan ML
60
-55-
V1
60-
55
V1.2
Minkeennn Lia
Mikkeenn, Mi
Chup
Chap
50-
45
45
348
Pro
45-
40
40-
(XX.Y -0887
PXXY] =0.91
40
PXXY 0095
70
72
74
76
78
80
35
55
60
65
70
75
30
35
40
50
True/false Correct Rate (%)
Choices Correct Rate (%)
VOA Correct Rate (%)
Figure 6: Visualization of the relationship between the correct rate of discriminative answer and its Consistency with the caption on different answer types.
We analyze the answers that fail in all three question types and find that, despite all resulting in incorrect predictions, they do not demonstrate consistent understanding of the same images, leading to distinct answers. For example, we calculated the proportion of consistent incorrect responses in VQA and multiple-choice questions. We found very small consistency, and it did not exceed 0.50% across the entire benchmark. This indicates that the models struggle to interpret the visual content uniformly, revealing significant variability in their failure modes.
Fact 4.2.3 (Inconsistency in Confidence). The confidence of models in their answers reveals signs of inconsistent and incorrect predictions.
Taking Fact 4.2.1 and Fact 4.2.2 into account, we perform a deeper analysis of the model's s predictions by measuring their confidence in the answers. We use the predicted probabilities and logits of the answer tokens to represent confidence (see Appendix for details). As summarized in Figure 5, we measure the average probabilities and logits of the correct and incorrect answers", respectively. The three types of questions share similar confidence levels for the correct answers. However, for the incorrect answers, their confidence levels vary significantly with clear trend: the larger the solution space, the smaller the confidence. This analysis provides crucial insights for our method in enhancing the consistency and accuracy of LVLMs, which will further discuss in Sec. 5.
4.3 Generative Domain
Next, we extend our attention to the generative domain. Based on Consistency, we first build a bridge between the discriminative and generative domains. We consolidate our findings as the below facts:
Fact 4.3.1 (Inconsistency to Generative Answers). As the solution space of discriminative questions increases, the Consistency between their answers and generative answers increases.
As indicated in the last column of Table 2, "Ordered" means Con[T] Con[C] Con[V]. The answers of all closed-source models and most open-source models adhere to this pattern. Here is the theoretical explanation. Assume the distribution for the generative domain (Caption) is S, and the sample space of S is W. For the discriminative domain, the sample space is limited to W", which only contains some candidates from W. Assume the model handles the discriminative domain by creating another distribution S' according to S and W', Then the total variation distance (TVD) [11]
'MGM_Y and LLaVA Y mean the correct, while MGM N and LLaVA. NN represent the wrong.
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Sensation
Cognition
Knowledge
PN
70
GFT-42
50
Gown Pax
45-
65
Gamni nu Po
50
60-
Chot
V1.
Consisterry Rate Chaices
Lunk 130
LLack
55
Pg
Chil
mr
50
30
340
135
336
85
PXX,Y 00.87
28
78
PIX.Y m0.89
20
20
60
Choices Correct Rate (%)
Choices Correct Rate (%)
Choices Correct Rate (%)
Figure 7: Visualization the relationship between the correct rate discriminative answer and its Consistency with the caption on different capability types.
between S and S' is
|| - S'Il
This becomes larger when [W WI becomes larger. For instance, if the model creates by simply doing reject sampling', then
2 1|8- ||8- = E W WW).
is obvious that when W' is more "different" from W, the distance will be larger.
Fact 4.3.2 (Connection between Discriminative and Generative Domain). The accuracy of the discriminative answer exhibits a strong positive correlation with its Consistency with the generative.
As shown in Figure 6 and 7, we conduct visualizations for all tested LVLMs: sents the accuracy of their discriminative answers, while the horizontal axis represents the consistency of the answers with caption. Figure displays the distribution across different question types, while Figure 7 illustrates the distribution across different core capabilities. The green lines represent fitted linear equation. Additionally, utilize the Pearson coefficient P(X, Y) to quantitatively analyze the degree of linear correlation, and the 6 coefficients in the above figures are more than 0.85.
The vertical axis repre-
4.4 Consistency Bias
Fact 4.4.1 (Consistency Bias). Closed-soure models exhibit a pronounced bias advantage on Consistency, compared to open-source models.
When we fit linear regression to all evaluated models and get the green line in Figure (a):
L : kr b,
where x is the accuracy, and y means Consistency between its answer and caption. We found that the majority open-source models below this line, while closed-source models lie above it. other words, at the same level of accuracy, the responses from closed- closed-source source models tend to exhibit better consistency with their captions. So we fit linear regression to closed -ourcee models and get the red line. The line they reside has higher bias be (c.g.. b b 3.24 in Figure 6 (a)), which aligns with our experience where closed-source models provide more comprehensive and reliable answers.
Trigger-based Diagnostic Refinement
In light of the previous findings, summarize two key insights: (1) LVLMs exhibit higher accuracy when operating within a narrower discriminative solution space; (2) Incorrect answers are usually propose a simple but
associated with significantly lower confidence and logits. Consequently, efficient method dubbed Trigger- Trigger-based Diagnostic Refinement (TDR) to ameliorate the generation skill of LVLMs without any additional training. The proposed pipeline is presented in Figure 8.
'e.g, projecting clustering S to W
8

---

Table 4: Results on LLaVA-34B and MiniGemmini-34B via Trigger-based Diagnostic Refinement.
Method
ConScore[C]
Co
Con|C]
Con|V]
LLaVA-NeXT-34B [19]
48.3
16.00
52.20
46.80
+ +DR
57.4 (9.1 1)
69.10
57.40
45.70
MiniGemini--44B [15]
49.6
56.80
48.00
44.10
TDR
60.2 (9.6 1)
76.10
53.80
50.80
Method We start by making the LVLM generate caption, with each word accompanied by its corre- sponding probability. Next, uninformative words are dropped based on their parts of speech, and we only keep nouns, adjectives and quantifiers. When the re- maining words with probabilities below a threshold T (we set 7 0.85 here), trigger subsequent diagnos- tic processes. Since low probabilities of words indi- cate a lack of confidence, we formulate True/False discriminative questions to force the LVLM to self- verify (e.g., Is there {cat} in the picture?). The self-diagnostic prompt and its response will be drafted into a new prompt, which is fed back into the LVLM generate higher-quality caption.
Prompt: Please describe this image.
LVLMs
cat
walks
on
the
street
0.98
0.72
0.99
0.99 0.96
0.93
?
Q: Is there (cat) in the picture?
LVLMs
A
No
Re-Prompt:
We asked that (Q) And (A/
Results We carried out experiments on the LLaVA- NeXT-34B and MiniGemini-34D and evaluated them on the metric[C] of ConBench. The experimental re- sults are detailed in Table 4. Notably, the LLaVA- NeXT-34B sees an improvement of 9.1 points, while the MiniGemini experiences overall enhancement of 9.6 points. Although approach primarily employs True/False questions for self-verify, there is still a noticeable improvement in ConScore(C]. Hence, method effectively boosts the quality of captions by triggering the model to self-check.
your answer. Please describe image again.
Figure 8: The Trigger- based Diagnostic Refinement pipeline.
In theory, we can further construct multiple discriminative questions for the caption, enabling the model to verify multiple elements within the caption. Additionally, the process can be iterated multiple rounds, leading to ongoing enhancements in the quality of the generated output. Our method is simplified implementation of the above approaches.
6 Conclusion
In this study, we investigate the Consistency issues in large vision-language models (LVLMss. Consistency reflects the overall ability of LVLMs, as it not only requires LVLMs provide correct answers but also demands sufficient confidence in their knowledge point, regardless of the type of question encountered. We first introduce the ConBench, benchmark that fills the gap in assessing Consistency. It includes IK images with 4K prompts and two evaluation metrics: ConScore(D) and ConScore[C]. Then, our findings shed light on the nature of Consistency in LVLMs according the ConBench. We observe that as the solution space of a prompt increases, the accuracy of the answers tends decrease. Besides, we establish a relationship between the discriminative and generative realms, highlighting the importance of Consistency between the discriminative answer and caption. Furthermore, we discover that closed-soure source models exhibit a bias advantage over open-source models in terms of consistency. Finally, we propose solution by forcing LVLMs to self-think, where a discriminative prompt is constructed via uncertain words in the caption. Our method makes the quality of LVLMss captions an impressive achievement. We believe that our research contributes to the evaluation of L.L.Ms and encourages future advancements for achieving Consistency LVLMS..
Acknowledgment
Shanghang Zhang supported by the National Science and Technology Major Project (No. 20222D001120 17800).

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Appendix
A Showcases of Trigger-based Diagnostic Refinement
MGM-34B
Image Count-000000026621....
Original Caption. The image contains a variety of objects, which can be counted as follows: one tray, one cake, one bowl of sauce, carton of milk, banana, glass of orange juice, cup of coffee, and one sugar packet. The objects are arranged neatly on the tray, which is placed a table.
Refined Caption The image contains total of two bananas, which are positioned on the right side of the tray. The bananas are yellow, indicating that they are ripe, and they are the only fruits visible in the image. They are placed next to carton of milk, ice cream, a cake, two cups of coffee, a glass of orange juice, which are a common accompaniment in a breakfast setting.
LLava-Next-34B
Image. Attribute Reason- 259.441466.90024444....g
Original Caption. The image captures vibrant live music scene. Ar the center of the frame, a musician is seated on a stool, engrossed in playing a red electric guitar. He is dressed casually in white t t-shirt and blue jeans, adding the relaxed atmosphere of the performance. The stage, adorned with a colorful banner, is set against a backdrop of a brick wall, lending an urban feel to the setting. The banner prominently features the text "DOWNTOWN 5", possibly indicating the name of the band or the event. The musician is surrounded by other band members, each engrossed in their own instruments, contributing to the overall harmony of the performance. The stage is well-equipped with microphone and speaker, essential for amplifying the sound and reaching the audience. In the background, a crowd of people can be seen, their attention riveted on the performance.
5
Refined Caption The image captures a vibrant scene of a live music performance. At the center of the stage, musician is seated a stool, engrossed in playing an electric guitar. He is dressed in a casual white 1-shirt, and his focus evident as he strums the strings. The stage is set against backdrop of colorful banner that proudly proclaims "DOWNTOWN 5". The banner's bold colors contrast with the musician's 's white t-shirt, drawing attention to both the performer and the event. The stage is equipped with various musical instruments, including a drum set and keyboard, suggesting a full band performance. A microphone stands ready, capturing the musician's 's performance for the audience. The stage is situated in space that appears to be partially open the elements, as suggested by the presence of a roof structure in the background. This setup indicates that the concert is being held in venue that can accommodate outdoor unique ambiance to the event. Overall. the image portrays a dynamic and engaging live music event, with the musician at the heart of the performance, surrounded by his instruments.
remi-outdoor performances, adding
B Computation for Probabilities and Logits
For each model (MGM-13B and LLaVA-13B), we randomly selected 50 consistent and 50 inconsistent samples, respectively. The JSON samples are in support materials. To measure the LVLMs' response confidence accurately, we manually simplified the answers and maintained the words that are the most critical the judgment. The specific principles are listed:
For true/false questions, we only keep the word 'yes' or 'no' and their probabilities.
e. g.g., [Yes], there is cat.
For multiple-choice questions, only keep the choice labels (c.g., A, B. C) and their probabilities.
12

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g. ([A]) Cats.
For limited VQA questions, we manually picked out keywords that matched ground truth from answers, and computed the average probabilities of them as the final probability.
e g. A [Cat] walks on the street.
C
Limitations
The introduced ConBench offers a new perspective on evaluating model performance through the consistency between multiple types of questions, providing a more comprehensive measurement and understanding of existing LVLMs. However, due the distinct response forms of captions, assessing the consistency between captions and discriminative answers is judged by GPT, posing a risk of inaceurate evaluations. Besides, by delving deeper into our benchmark analysis, we propose trigger- based diagnostic refinement to improve the consistency and accuracy of LVLMs. This, however, introduces additional computational costs and is limited by the inherent capabilities of the LVLMs. Further improvements can be achieved by designing and training LVLMs with focus on consistency.
D Broader Impacts
Overall, this research has broader impacts on the evaluation, performance, fairness, and future development of LVLMs, fostering progress and advancements in the field of vision-language models.
Advancing Evaluation:
The introduction of ConBench, a benchmark for assessing Consistency in LVLMs, fills a crucial gap in the evaluation of these models. This benchmark provides a standardized framework for measuring the performance and reliability of LVLMs across different prompts.
we are the first to reveal the tapestry and get the following findings: (1) In the discriminate realm, the larger the solution space of the prompt, the lower the accuracy of the answers. (2) Establish the relationship between the discriminative and generative realms. (3) Compared to open-source models, closed-source models exhibit a bias advantage in terms of Consistency.
Novel Insights:
Inspiring Future Research: By contributing to the evaluation and understanding Consistency in LVLMs, this research paves the way for future advancements in the field. It encourages researchers to explore new techniques, methodologies, and approaches to achieve higher levels of Consistency in LVLMs, ultimately pushing the boundaries of language and vision understanding.
E Detailed Cases in ConBench
We have uploaded the ConBench dataset, including images and their prompts, to the Hugging Face platform. The dataset can be accessed the following URL: Ciassets/ConBenee ConBench. Here, we enumerate several representative cases from ConBench. Arrange in order from easy difficult, respectively, based on sensation, cognition, and knowledge.
co/
13

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Count
Are there three laptops in the picture? Please only ansuer yes or no. Yes
How many laptops the picture?
B) Two
Three
D) Four.
many laptops depicted?
Please choose an answer from [A, B, C,
Please answer with number. A
D).
A:
Caption: You are an expert image description. You need describe this picture with accurate object count information.
Scene
T: Is this photo taken in place of corridor? Please yes A: Yes
Where was this photo taken?
Corridor
B Park
Office
Street.
Please choose an ansuver from [A, B, C,
V: Where the photo taken? Ansueer withii word. Corridor
D)
A:A
Caption: are expert scene. You need to describe the scmee in the picture.
OCR
Ts word in the logo
What is the word in the referenced
\ l'angle's' Please ansuer yes No
logo?
ANGLE'S
Angie's
Angie
C)
Agues's
Anjle's.
V: What is the word in the refermnced
Please choose answer from [A, B,
logo? Answer within woodd At Angie's
D].
A:A
Caption: You expert image
OCR. What is the word the referenced logo?
Position
T: the white couch positioned behind glass coffee table? Please muswer A:
C: What is the position of the white couch relative glass coffee table?
The couch is front of the coffee table
The couch is right the coffee table
Where the white couch located in relation to the glass coffee table? words.
The is left of the coffee
table
Please answer
The couch beltnnd the coffee table. Please chouse answer from (A, B, D).
As behind the coffee table
Caption: are expert position. You need to describe this picture with accurate information about position about objects in the image.
14

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Math
T: function conrexx? Please answer or no.
C: finction
conver or concave?
Ar Yes
A) Convex
x² 6x+4
B) Concavee
C Neither
f f(x)
Both.
Is the function fix) x*2 fx concave? Ansueer withii word.
Please choose an answer from [A, B, C,
A:A
Caption: You are an expert in math.
Describe concave comnex
properties the function.
Code
T: The image shows python code. the output of the code 'World?? Please only tell 'yes' without any other words. As
What is the outpptt the Python code?
Goodbye
if 20:
B) Hello
print ("Hello")
D) Nothing.
else:
Choose one from four letters [A, B,
D] without any other words.
print ("World")
V: What output Python code? Please answer with one word without any other words.
A:B
Caption: You expert code reasoning. What the programmming language code and tell the oufput without any other words.
A: Hello
Translation
T: appropriate translate the Chinese in the image into English 'a delicious dismer' the picture?
How translate the Chinese
image into English?
delicious dinner
Please answer yes A: Yes
B) traditional flavor hamburger chips
vintage taste.
Please choose an ansuver from [A, B, C,
D).
V: Translate the Chinese the picture to Englishh, Answerr within 3 words. At a delicious dinner
ArA
Caption: are expert translation. You need to translate the Chinese this picture into English.
Attribute Reasoning
T: position and activity the horses indicatice of them eugaging competitive playful interaction nather than stationary activities like grazing or standing still? Please yes As
C: Based the attributes and positions of the horses, whichh
conclusion could drawn?
A) The horses grazing
B) The horses all standing still
C) The horses playing togelferr
D) The horses are racing.
Please choose answer from IA,
D).
V: Based the attributes and positions of the horses, whick conclusion could be drawn? Answer within words.
Caption: expert attribute reason, please descrthe this image in
are grazing
15

---

Artwork
T: Does this artwork exist in the form painting? Please yes Az Yes
C: which form does this artwork exist?
A) Sculpture
Painting
C) Digital Art
D) Performance Art.
V: What is the form of this artwork?
Please choose answer from (A, B,
Please answer within 3 words.
DI.
Painting
Caption:
expert artwork. You need to describe this picture with
accurate information about its title, actor, form and the location of display.
Celebrity
T: Is the actor inside the red bounding box called Hugh Jackman? Please answer no.
C: Which actor identifiod within the bounding box?
A) Tom Cruise
B) Robert Downey Tr
C Hugh Jackman
D) Chris Hemswortff.
V: Who is the actor identified inside the red bounding box? Please answer with within words. As Hugh Jackman
Please choose answer from (A, C,
D).
Caption: description. You need to deseribe this picture with accurate information about the actor.
expert image
Biology
Does
anode?
apparatus in figure used for SDSPAGE (polyacrylamiaa gel electrophoresis). Which letter indicates
Please answer yes no. No
the anode?
A)
apparntus figure used
O Neither
for SDSPAGE (polyacrylamide gel electrophoresis), Which letter indicates the anode? A:
D) Unknown.
Please choose answeer from (A, C,
Plade
D).
B
Caption: You are an expert in biology. The apparatus figure used for SDSPAGE (polyacrylamide gel electrophoresis) Which indicates the anode?
Landmark
T: this a photo of Great Palace Mosaic Musemm? Please answer yes No
C: Which the following landmarks
is pictured this photo?
Eiffel
B) The Leaning Tower Pisa Pisa, Itlly
Q The Dom Tower Utreccht, Netherlands
V: In vhhich city can fiud the landmark shown the picture? Ansuver within words.
London, England, Chouse one from the four letters IA, B, C, D).
D)
A: Utrecht
A:
Caption: You are an expert in landmark. You need describe this picture with accurate location information.
16

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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope, including a benchmark, three main analyses and a method improve the caption of LVLMs.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We point out our limitations of the work in Appendix C.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sellsspeciiiationn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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Answer:
[NA]
Justification:
The paper does not include theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proof's can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
Justification: Our paper releases dataset and evaluation method, and the experimental results are reproducible.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, descríbing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
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Reviews:
[{"review_id": "FETePMpDcD", "reviewer": "Reviewer_AnYL", "summary": "The paper presented a comprehensive study LVLMs on their inconsistent answers given different prompt solution spaces (true/false, multiple choice, and limited QA). Specifically, the authors introduced the ConBench benchmark to evaluate the performance of various models. They investigated the relationship between the size of the solution space and the accuracy of answers provided by these models, and analysed the consistency between answers and the generated captions. They also presented a simple yet effective method to ameliorate the consistency.", "questions": "See above for questions.", "limitations": "The authors discussed potential social impacts as well as limitations of their paper in the appendix. I agreed with them that using GPTs for evaluation might pose a bias and a more rigorous human evaluation was preferred.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* It is an interesting perspective to construct a benchmark to evaluate the model's performance when prompt with different solution spaces are provided. In fact, this is a common problem that we might encounter when we used LVLMs to answer our questions.\n* The analysis was comprehensive with some insightful findings. In particular, the positive correlation between the accuracy and consistency, and the relationship between the confidence and consistency.\n* The authors also provided a simple yet effective method, i.e., picking those words with lower confidence scores for re-prompting, to generate more accurate captions. The method did not require re-training the model.", "weaknesses": "* The proposed benchmark was not sufficient to evaluate the model, with the size of only 1K images, and three types of questions. It would be great if the bench can be more diverse and representative for various domains.\n* It was not clear what was the insight for Fact 4.4.1. Did the authors want to show that closed-source models put more efforts on improving the consistency compared with open-sourced ones?\n* The current method to improve consistency was ad-hoc and based on such an idea, it is likely that simply changing the generation arguments like temperature, top_p or top_k might have similar effects.\n* The paper seems more suitable to be submitted to the Datasets & Benchmarks track instead of the main track.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer AnYL for the efforts in reviewing our paper. Our responses according to the reviewer's comments are summarized as follows.\n\n---\n\n> 1. It would be beneficial to increase the diversity and scale of ConBench.\n\nWe greatly appreciate your attention to the scale and diversity of ConBench. The current version of ConBench contains 1,000 images with 3,000 questions, and covers 19 subdomains, comparable to popular multimodal datasets such as MME [1] and MMBench [2]. In this version, multimodal models have not yet achieved high scores (e.g., the current best model Qwen-VL only achieves 37.00 on ConScore[D]). With further development of VLMs, we will update and scale up the cases of benchmark datasets to tens of thousands, providing a more comprehensive evaluation of Consistency.\n\n[1] MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.\n\n[2] MMBench: Is your multi-modal model an all-around player?\n\n---\n\n> 2. Is the Fact 4.4.1. to show that closed-source models put more effort into improving the consistency compared with open-sourced ones?\n\nIn the paper, we discovered Fact 4.4.1 to provide an alternative perspective to demonstrate why closed-source models, despite sometimes having lower accuracy on benchmarks, offer a better user experience in practical applications. One possible reason, based on our paper, is that closed-source models have a Consistency bias. Therefore, evaluating the Consistency of VLMs is a feasible approach to indirectly quantifying and assessing the actual user experience.\n\n---\n\n> 3. Changing generation arguments like temperature, top_p or top_k might have similar effects with the proposed method TDR.\n\nIn fact, adjusting these hyperparameters only affects the randomness in sampling, not improving the quality of the generation. As shown in the following table, we conducted comprehensive experiments on LLaVA-NeXT-34B and MiniGemini-34B. The temperature and top_p in the baselines are $0.0$ and $None$, and we set temperature to $0.2/0.8$ and top_p to $0.9/0.7$ in the ablation studies. The results show that tuning the temperature or top_p values can not enhance the Consistency of model responses. In contrast, our approach makes the model aware of where lacks confidence and has errors, leading to corrections that boost generation quality. Therefore, changing generation argument hardly achieves similar effects.\n\n| Method       | ConScore[C]   |  Con[T]  |Con[C] | Con[V]\n| :--------  | :-----  | :----:  | :----:  | :----:  |\n| *LLaVA-NeXT-34B (baseline)* |  48.3 |  46.00 | 52.20 | 46.80 |\n| + temperature (0.2)| 48.0| 45.75 | 51.75| 46.51|\n| + temperature (0.8)| 44.1|42.55 | 47.15 | 42.57|\n| + temperature (0.2), top_p (0.9)| 47.8| 45.57 | 51.72 | 46.12|\n| + temperature (0.2), top_p (0.7)| 48.2| 45.88 | 52.12  | 46.60|\n| + TDR (ours) |  **57.4**|  69.10 | 57.40 | 45.70 |\n| | | | |\n| *MiniGemini-34B (baseline)*|  49.6 | 56.80 | 48.00 |44.10 |\n| + temperature (0.2) |48.9|56.00|47.32 | 43.38|\n| + temperature (0.8) |45.6|52.22 | 47.99 |36.59|\n| + temperature (0.2), top_p (0.9) | 48.1| 55.08 |50.62 | 38.60 |\n| + temperature (0.2), top_p (0.7)| 49.1 | 55.97 | 47.64 | 43.69 |\n| + TDR (ours)  |  **60.2** | 76.10 | 53.80 | 50.80 |\n\n---\n\n> 4. The paper seems more suitable to be submitted to the Datasets & Benchmarks track instead of the main track.\n\nOur paper differs from the papers in the Datasets & Benchmarks track. It not only introduces a new dataset ConBench but, more importantly, we provide deep analyses of the Inconsistency phenomenon based on that. Our findings establish a relationship between the discriminative and generative realms, highlighting the importance of Consistency between the discriminative answer and caption. Moreover, we propose a solution by forcing VLMs to self-think, where a discriminative prompt is constructed via uncertain words in the caption. It is a comprehensive evaluation pipeline instead of introducing the dataset or benchmark solely.", "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": "zG8F1tGoNl", "reviewer": "Reviewer_aYRC", "summary": "This paper presents ConBench, a multi-modal benchmark to intuitively analyze how LVLMs perform when different prompts are used for one model around a knowledge point. Based on the proposed benchmark, several interesting findings are pointed out, such as the relationships between the prompt space and the answer accuracy in both the discrimitive and generative realm. Based on the findings, the paper proposes a way to improve  the consistency of LVLMs by trigger-based diagnostic refinement. This is specifically conducted on improving the caption capabilities of LVLMs.", "questions": "Shown in the weaknesses part\n\nJustification of final rating:\n\nI appreciate the opportunity to evaluate this submission. Overall, the proposed benchmark is interesting. The findings based on the benchmark are reasonable. The proposed methods based on the findings are practical. I give my rating as boardline accept.", "limitations": "The author has claimed it in the checklist.", "rating": 5, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The benchmark is comprehensive and the related findings are interesting.\n2. The motivation for the proposed method to improve the consistency of LVLMs is straightforward based on the findings.\n3. The evaluations conducted on the designed benchmarks provide evidence of the effectiveness. However, there are some concerns regarding the experimental results, which will be further discussed in the weaknesses section.", "weaknesses": "1. Some of the presentations are unclear. For example, the definition of the discriminative and generative domains should be clearly explained at the beginning to avoid  obscure understandings.\n2. Some metrics are not clearly explained, including but not limited to ConScore[C], metric[C] and metric[D] in Sec 4.1.\n3. For the generative problem, the paper uses GPT4 to compare the consistency of the LVLM output answer and the ground truth. However, several issues arise here: (1) Does GPT4 have a certain bias; (2) Does GPT4 also have inconsistent judgment issues; (3) How to eliminate the impact of GPT4's inconsistent judgments, for example, is it feasible to conduct several experiments and provide results variation.\n4. About the Trigger-based Diagnostic Refinement: it seems that previous work [a] also used the same way to give more accurate answers in LLM realm, that is prompting the models with previous answers and the same-meaning questions. Further explanations are suggested to clarify on the common and different things between the proposed Trigger-based Diagnostic Refinement and other works (e.g., [a]) in LLM realm.\n\n[a] Chuanyang Zheng, etc.  Progressive-Hint Prompting Improves Reasoning in Large Language Models， 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer aYRC for the efforts in reviewing our paper and positive evaluation. Our responses according to the reviewer's comments are summarized as follows.\n\n---\n\n> 1. Explain the definition of the discriminative and generative domains clearly.\n\n(1) Discriminative questions provide prior information about the image in the text, restricting the model to answer based on this information, making it a closed-ended question.\n\n(2) Generative questions, on the other hand, do not provide the model with specific image priors, allowing the model to generate answers totally based on its own reasoning, making it an open-ended question.\n\n(3) Furthermore, we will carefully review the clarity of other presentations and make corrections in the revised version\n\n---\n\n> 2. Explain the definition of ConScore[C], metric[C], and metric[D].\n\n**(1) ConScore[C]**\n\nThe ConScore[C] can be clearly defined by the following formula:\n\n        ConScore[C] = (Con[T] + Con[C] + Con[V]) / 3,\n\n where the $Con[X]$ is the Consistency ratio between discriminative answer type $X$ and the caption. The ConScore[C] is a generative domain evaluation metric to indirectly quantifying and assessing the actual user experience.\n\n**(2) Metric[C] and Metric[D]**\n\nMetric[C] and Metric[D] actually refer to the tables with ConScore[C] and Score[D] as evaluation metrics, respectively. We will provide clearer explanations in the revised version.\n\n---\n\n> 3. The concerns about using GPT4: (1) Does GPT4 have a certain bias; (2) Does GPT4 also have inconsistent judgment issues; (3) How to eliminate the impact of GPT4's inconsistent judgments?\n\n(1) Initially, we only employ GPT-4 to determine the Consistency of model responses, (e.g., prompts like \"Based on the caption, is my response correct? Please answer with yes or no only.\"). Therefore, this is a binary choice task for GPT-4, and our primary focus is on its accuracy. Regarding this task, we manually checked its performance on five different VLMs by randomly sampling 200 cases in each trial. As shown in the following table,  the GPT-4 achieves an **accuracy of 95.4% (until August 4, 2024)** with standard deviation of 0.0035, which is highly reliable and stable.\n\n| Responder  | Accuracy rate of GPT-4 (6 trials)\n| :--------  | :-----  |\n|GPT-4V |  95.1%\n|Gemini-Pro-Vision |  95.8%\n|Qwen-VL-Max | 94.9%\n| LLaVA-NeXT-34B | 95.7%\n|InternVL-v1.2P-40B | 95.5%\n\n\n(2) Inconsistency refers to the model providing inconsistent answers when faced with different solution spaces of prompts for the same knowledge point. Based on the above, GPT-4 in our evaluation always answers 'yes' or 'no', where the solution space of prompts remains the same, so the concept of Inconsistency does not need to be considered in this situation.\n\n---\n\n> 4. The comparison between the proposed Trigger-based Diagnostic Refinement and other works (e.g., [1]) in the LLM realm.\n\n**(1) Similarity**\n\nThe similarity between ours and [1] lies in asking follow-up questions based on the historical responses of the model and confirming the final answer, to enhance the quality of responses.\n\n**(2) Differences**\n\nHowever, the motivation and technical details of ours and [1] are completely different. [1] utilizes all the accumulated responses as options for the next round of prompts, iteratively searching through historical responses to find the correct answer, which is just effective for mathematical and reasoning abilities. Our method only picks out the uncertain words in model responses, and constructs discriminative templates for questioning, proving effective comprehensive abilities.\n\n[1] Chuanyang Zheng, etc. Progressive-Hint Prompting Improves Reasoning in Large Language Models, 2023.", "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": "sMr6n6Va1S", "reviewer": "Reviewer_wv4L", "summary": "This paper introduces a multimodal Consistency Benchmark (ConBench) to systematically evaluate the capabilities of LVLMs via diverse question formats. \nConBench has a total of 4k questions on 1k images and corresponding 3k discriminative ground truths, as well as two special metrics to evaluate the consistency of LVLMs. \nBased on ConBench, this work conducts a comprehensive analysis of inconsistency in LVLMs.", "questions": "See Weakness", "limitations": "Yes, the authors discuss limitations in the Appendix.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The constructed ConBench can better evaluate LVLMs and encourage further advancements in the consistency domain. \n- Their findings provide insight for future community research.\n- This paper proposes a trigger-based diagnostic refinement method (TDR) to ameliorate the generation skill of LVLMs without any additional training.", "weaknesses": "- The results in Table 4 suggest that TDR can markedly improve the consistency of baseline LVLMs. In addition to ConScore[C], how does TDR affect LVLMs' performance on comprehensive multimodal benchmarks?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the efforts in reviewing our paper and the positive evaluation. Our responses according to the reviewer's comments are summarized as follows.\n\n---\n\n> 1. In addition to ConScore[C], how does TDR affect LVLMs' performance on comprehensive multimodal benchmarks?\n\nOur proposed method TDR aims to utilize the discriminative prompt template and VLMs' judgment capability to enhance their open-ended generation ability. \n\nGiven the difficulty in evaluating open-ended responses, we randomly selected 100 images from MMMU, MMBench, and MMStar. Then, LLaVA-v1.5-7B was employed to generate initial captions for these images, which were then improved by our TDR method. After conducting five rounds of manual evaluation, we observed that the TDR method resulted in improvements in 37% of cases, no change in 60% of the cases, and a slight decrease in 3% of the cases (**GSB = 37:60:3**). The results further confirm the effectiveness of the TDR approach.\n\nFurthermore, while not directly for the improvement of discriminative responses, motivated by your question, we also attempted two discriminative benchmarks (rewriting prompts for some low-probability answers). As shown in the table below, we are pleased to see that our method improves LLaVA-v1.5-7B by $0.84$ and $0.92$, and boosts MiniGemini-7B by $0.53$ and $0.79$ on GQA and POPE, respectively. These promising results will inspire our further research.\n\n| Method       | GQA   |  POPE\n| :--------  | :-----  | :----:  |\n| *LLaVA-v1.5-7B (baseline)* |  61.94 |  85.88|\n| + TDR (ours) |  **62.78**| **86.80**|\n| | | |\n| *MiniGemini-7B (baseline)* |  63.66 |  87.65|\n| + TDR (ours) |  **64.19**| **88.44**|", "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:
Dear ACs and Reviewers,

We thank all the reviewers for their valuable comments and efforts in reviewing our paper.

We are delighted that Reviewer AnYL, aYRC, and wv4L stated that our findings are interesting and the benchmark is comprehensive; Reviewer AnYL and wv4L acknowledged that our method TDR is efficient and has a technical contribution.

Regarding the questions and concerns of the reviewers, we have provided our responses in their respective sections.

Meta-review: This paper presents a new multi-modal consistency benchmark, called ConBench, to analyze how LVLMs perform when the solution space of a prompt changes. The benchmark consists of 1000 public images from available benchmark with their original prompt and 2 additional discriminative type of questions from gpt4. All reviewers gave slightly positive feedback (2 borderline accept and 1 accept). All reviewers agree that the proposed benchmark is interesting and provides insight for the community on the relationship between consistency and accuracy. In additional, the proposed trigger-based diagnostic refinement method is simple, practical and effective on the new benchmark. There are also some concerns raised by reviewers: Reviewer AnYL thinks that 1k is very small volume as an evaluation benchmark and the types of question is also limited, we would need more diverse images and questions with more domains; Reviewer aYRC questions the accuracy of using GPT4o as judge, and delta with another trigger-based method; Reviewer wv4L wants to understand improvement of the proposed method on other benchmarks. The authors have provided rebuttals to address these questions. After reading all reviews and rebuttals, the AC agree with reviewers and would like to recommend accepting this paper. AC wants to encourage authors to scale up this benchmark to tens of thousands and increase the diversity, so that the community can widely adopt this benchmark.
Decision: Accept (poster)