LOVA3: Learning to Visual Question Answering, Asking and Assessment
Authors: Hengyuan Zhao
Pan Zhou
Difei Gao
Zechen Bai
Mike Zheng Shou
Venue: NeurIPS 2024 poster (2024)
Keywords: Multimodal Large Language Model
Instruction Tuning
Visual Question Answering
Visual Question Assessment.
Primary area: machine_vision
TL;DR: In this work, we present  a novel ``Learning tO Visual Question Answering, Asking and Assessment'' framework that can enable the current MLLMs to obtain the additional abilities to ask and assess the question based on the visual input.

Abstract: Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can more effectively utilize data, leading to better comprehension and learning outcomes. However, current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. In this study, we introduce LOVA3, an innovative framework named ``Learning tO Visual Question Answering, Asking and Assessment,'' designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming at fostering the skills of asking and assessing questions in the context of images. To develop the questioning ability, we compile a comprehensive set of multimodal foundational tasks. For assessment, we introduce a new benchmark called EvalQABench, comprising 64,000 training samples (split evenly between positive and negative samples) and 5,000 testing samples. We posit that enhancing MLLMs with the capabilities to answer, ask, and assess questions 
will enhance their multimodal comprehension, ultimately improving overall performance. To validate this hypothesis, we train MLLMs using the LOVA3 framework and evaluate them on a range of multimodal datasets and benchmarks. Our results demonstrate consistent performance gains, underscoring the critical role of these additional tasks in fostering comprehensive intelligence in MLLMs.

LOVA 3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao', Pan Zhou?t, Difei Gao', Zechen Bai', Mike Zheng Shou
"Show Lab, National University of Singapore, 2 Singapore Management University
Abstract
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabil- ities, humans can more effectively utilize data, leading better comprehension and learning outcomes. Current Multimodal Large Language Models (MLLMs) primar- ily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA3, an innovative framework named "Learning tO Visual question Answer- ing, Asking and Assessment," designed to equip MLLMs with these additional capabilities. Our approach involves the creation of two supplementary training tasks GenQA and EvalQA, aiming at fostering the skills of asking and assessing questions in the context of images. To develop the questioning ability, we compile a comprehensive set of multimodal foundational tasks. For assessment, a new benchmark called EvalQA Bench, comprising 64,000 training samples (split evenly between positive and negative samples) and 5,000 validation and testing samples. We posit that enhancing MLLMs with the capabilities to answer, ask, and assess questions will enhance their multimodal comprehension, ultimately im- proving overall performance. To validate this hypothesis, we train MLLMs using the LOVA framework and evaluate them on a range of multimodal datasets and benchmarks. Our results demonstrate consistent performance gains, underscoring the critical role of these additional tasks in fostering comprehensive intelligence MLLMs. The code is available at http:///hhhhb.coo//ooww////....
introduce
Introduction
To acquire knowledge, humans often answer lots of questions and then improve ourselves by comparing our answers with the ground-truth answers. As result, this learning mechanism empowers humans with the answering ability, which allows humans to handle well many real tasks, such as visual question answering [26, 54, 30, 27, 43, 42]. However, as described in the following slogan,
"The art of proposing a question must be held higher value than solving it." Georg Cantor [6]
asking a question is very valuable and even more important than answering question. Indeed, humans also acquire knowledge from learning to ask questions since it encourages individuals to engage more deeply with information, thereby enhancing problem-solving skills [69, 70, 21, 57]. In addition to asking questions, humans also improve themselves through self-evaluation: humans try to identify the correctness of the answer and thus are involved in deep understanding of our diverse world [86, 67].
Corresponniig author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Image
VQA Ability
GenQA Ability
EvaiQA Ability
Prompt: to thi filled disort Arnaser the question using o single word or pérane
Prompt: Anelyne the image and respond with
Prompt: Hlayue saamite the somectiens Ihis qurston and arower ascarding to the image contert Querton this fWle dosurlylkeseer
direct question and assow:
LLAWAL 5: What is the color of the pinki frosted pknouer Park
LLaVAL 5: Yes
LOUA"|Durs]
LLAVAL Yos
LOuel[Dun]: Veo
Queestion: What is the donut on the sede of the image culled?
LOVAY)oun]: No. this is filld donut.
Ground Truth: Yis
A. applle frittr long john C. cclar D. cruller Annwer:
Figure| Comparison of three abilities reveals that L excels in providing answers but struggles in asking accurate questions and assessing question-answer pairs.
Although innate humans, apart from answering ability, two other learning mechanisms of asking and assessment remain underexplored for contemporary multimodal large language models (MLLMs). Current MILMs [42, 84, 3, 95] are excelled in addressing diverse domains of multimodal questions such as mathematics [4], science [42], and commonsense knowledge [17]. However, they predom- inantly revolve around visual question answering (VQA). As result, as shown in Fig. 1, current MLLMs, e.g., the representative LLaVA-1.5 [42], suffer from inferior performance on asking ques- tions and self-assess question answer pairs (QA), which underscores their efficacy as problem problem-solvers solvers and prohibits holistic multimodal understanding.
To advance the comprehensive intelligence of MI.LMs, introduce two essential tasks: GenQA and EvalQA, aiming at bolstering the intelligence and robustness of MILMs. GenQA focuses on enabling the model to generate diverse question-anse answerr (QA) pairs from the single input image, thus equipping the MLLM with the capability to ask questions. We believe that if an MLLM can successfully generate QA pairs for challenging tasks, it indicates a higher level of problem-solving ability [40, 73]. Specifically, we define the GenQA task to include not only generic VQA (e.g.. VQAv2 [26] and GQA [30]) but also Multi Colti-Choice VQA (MC VQA), and Multi-Turn VQA (MT) to increase the variety of data formats. Additionally, we incorporate two challenging multimodal grounding tasks into the training process: Referring Expression Comprehension (REC) and Referring Expression Generation (REG). Learning to generate the data of these grounding tasks forces the MI.MM to extract fine-graing tneiggained visual cues from images, such as explicit object localization and compositional relationships. This, in turn, enhances the multimodal reasoning ability of MI.LMs. During training, we gather the relevant datasets for these tasks and transform them into a generative format using proposed instruction template. EvalQA, on the other hand, involves tasking the MLLM to predict the correctness of a given visual-question-answee triplet. Recognizing the absence of datasets specifically designed to assess VQA correctness, we have developed new benchmark called EvalQA Bench for evaluating VQA data. Rather than asking humans to label such a dataset, we propose new pipeline for data construction. This benchmark comprises training, validation, and test sets, with each VQA pair accompanied by a "Yes" or "No" label indicating correctness, along with one-sentence explanation as the feedback. For instance, "Yes, the oranges are not in a hag".
By integrating the GenQA and EvalQA tasks into the vanilla multimodal learning, we develop an effective training framework called LOVA3, In this study, select the SOTA MLLM 1L. as the backbone model for evaluation. We conduct experiments on 10 widely used multimodal benchmarks such GQA [30], VQAv2 [26]. Vizwiz [27]. MME [19], MMBench [45], and MM- vet [92], and observe consistent improvements across these benchmarks. To summarize, our proposed LOVA is new framework that endows the MLLM with the ability to ask and assess and finally achieve profound multimodal understanding capability. Overall, our contributions are three folds:
(I) To the best of our knowledge, LOVA 3 is the first effort to imbue the asking and assessment abilities in training robust and intelligent MLLM. LOVA open avenue for imitating the human abilities towards holistic intelligence for MLLM.
(2) We build new benchmark EvalQABench for the VQA evaluation as the first effort to advance the VQA data assessment of future research.
(3) The experimental results demonstrate that training with LOVA3 consistently improves per- formance across several multimodal benchmarks, including VQAv2, GQA, MME, VizWiz, MMBench, and MM-Vet, etc.
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2 Related Work
2.1 Multimodal Large Language Models
Large Language Models (LLMs) [16, 98, 65, 5, 81, 15, 82] such as GPT-4 [61] demonstrate their exceptional capacity to handle wide range of complex tasks to play an important role in assisting humans in daily life. Equipped with these LLMM, a surge of multimodal modes [37, 43, 17, 3, 10, 84, 101, 9, 8, 51, 85, 29, 62, 58, 88, 22, 47, 28, 34, 7, 100, 48, 14, 38, 24, 68, 23, 66] are proposed to integrate the visual information with the pre-trained LLM decoder for diverse multimodal reasoning tasks such as image captioning [12, 1, 91] and visual question answering [26, 54, 30, 27].
LLaVA [43, 42] is pioneering approach that collects 665K instruction tuning data from present vision-language (VL) datasets for supervised finetuning (SFT) and achieves promising results on various datasets in a lower cost of training requirement. Another SOTA model InstructBLIP [17] also proposes gathering datasets construct their instruction tuning dataset. It adopts the VQG task but is limited in generic data type. Different from InstructBIIIP we propose the GenQA task for jointly generated questions and answers on primary VI. tasks not restricted to generic data type. Besides focusing on traditional vision-language tasks, Shikra [10], Kosmos-2 [62], PVIT [8],
Ferret [90] pay attention to the image-region based multimodal tasks (i.e., Referring Expression Comprehension) and demonstrate the performance improvement with these hard tasks. By adopting large-scale image-text corpus for instruction tuning, Qwen-VL [3], CogVLM VLM [84], AnyMAL [58] and Chameleon [79] achieve exceptional performance
various multimodal tasks. However, these MLLMs primarily concentrate on training the model to answer questions as effectively as possible, neglecting the significance of enabling the model to act as a questioner and competent evaluator within the training paradigm.
2.2 Visual Question Answering and Generation
Nine years ago, visual question answer [2] was defined and became an essential task for evaluating
multimodal systems. A surge of VQA-related benchmarks [2, 26, 76, 56, 55, 27, 71, 54, 50, 30, 39] are emerged to advance the development of this research area, including generic VQA benchmarks [2, 26, 30], text-based VQA [76, 56, 55, 78], unowleege-augmented VQA [54, 71, 50, 13], and goal-oriented VQA [27] aimed at assisting blind people.
Besides the VQA task, the Visual Question Generation (VQG) task was first formulated in [60], which contributes a VQG dataset with each image annotated with multi-questions and benchmarking on generative models and retrieval models. [97] first employs an RNN-based encoder-decoder framework alongside model-generated captions to generate questions. After that a list of works [59, 60, 18, 32, 72, 87, 83] are proposed for promoting this research area. Two interesting studies [40, 73] pose that treating VQG as complementary task can enhance the robustness of visual question answering. This finding reaffirms our motivation that training a model generate diverse questions contributes to deeper understanding of visual information, thereby improving its problem-solving capabilities. Unlike the traditional Visual Question Generation (VQG) task, which focuses primarily on the generic VQA domain, GenQA is designed to generate diverse VQA data including MC VQA, MT, REC, and REG. Additionally, GenQA generates both questions and answers simultaneously, whereas traditional VQG focuses solely on question generation.
2.3 Multimodal Benchmarks
Traditional multimodal benchmarks focus on answering ability, such as visual question answering [26], image captioning [12, 63, 1]. as well as other benchmarks for specialized scenarios such as scene
text understanding [76, 75]. commonsense reasoning [94], outside knowledge [54, 71J. The recent development of MLLM posts a strong need for modernized multimodal benchmarks [19, 45, 36, 92, 25, 93, 49, 20, 11, 96, 44, 99, 77, 80] such as MME [19], MMBench [45], SEED-Bench [36] which involve comprehensively evaluating current MLLMs on various multimodal abilities. Unlike existing multimodal benchmarks focusing primarily on evaluating the model's ability to answer, we introduce EvalQABench, a benchmark designed to evaluate the correctness of VQA pairs, each with a binary "Yes/No" annotation. Furthermore, recognizing the lack of emphasis on providing feedback for incorrect answers in current benchmarks, we develop an LLM--bsed pipeline. This pipeline can automatically generate feedback, paving the way for enhanced automated data processing in the future.
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Table 1: Data taxonomy of GenQA, detailing the data type, name, size, and instruction prompts of each dataset.
Data Type
Dalane
Size
Instraction Prompts
VQAv2 [26]
100K
Noie: randemil chouse from 58 insiraction promppty
GQA [50]
100K
Example: Can you povide a clear question and its answer hased on the image?
Generic VQA
OCR SCR-VQABE [56]
80K
2ou
20K
LLaVA-25088 [43]
250K
Can you provide a clear question and its answer based on the image" ? ThThi is Malti choice VQA task.
Mulli-choice VQA
A-OKVQA [7]]
17K
VQAv [26]
83K
Design a conversaionn between you and a penson asking about this photo.
Multi-nu VQA
GQA [30]
72K
The answers should be question.
Uhit visual Al assistant seeing the image and answering the quesiions give corcsponding answers.
VG [33)
30K
Note: camdomil choose from 58 instraction prumpss with a speeific losk descriptionn promptt Can review the and articulate conclsee Expression Comprehension (REC)
and its anower") Thih is Referting question will express specific region the image.
REC
Ret000 [31]
30K
Please provide the coordinates in the answer.
Note: randomll chouse from 58 instraction pronpts with specific tusk deseription prompt, Can you review the image and articuliic a concise question and its amswer" Thiss is a Referring Expression Generation (RFG) look, The purpose of REG is to generate a unique description for specified location.
[3
30K
RPG
RefCoo [3]]
30K
Total
812K
3
Methodology
In this section, we introduce LOVA3, new framework designed to imitate two essential abilities asking and assessment within multimodal learning. We delve into the specifics of addressing this challenge through GenQA data collection, EvalQA data creation, model architecture, and training,
3.1 Data Collection for GenQA
If one MLLM is able to successfully generate high-quality question-answer pairs based on visual input, it indicates a stronger problem-solving ability and deep visual understanding [40, 73]. To enable the MLLM to ask questions, it is natural for us gather existing annotated datasets as the training corpus and then train the model to predict both questions and answers. We carefully define five main multimodal data types as listed in Tab. 1. For each data type, we gather widely used human-annotated datasets or high-quality instruction tuning datasets generated by GPT-4. We select Generic VQA tasks to generate fundamental questions, c.g., object count and object action. We incorporate Multi-choice VQA (MC VQA) and Multi-turn VQA (MT) to increase the diversity of data formats. Additionally, include two multimodal grounding tasks: Referring Expression Comprehension (REC) and Referring Expression Generation (REG). Generating REC and REG data requires deeper understanding of image content, enabling the model fully comprehend visual cues. Both tasks increase the difficulty of GenQA, which helps MLLM acquire a higher level of multimodal understanding. In total, we gather 842K data for training questioning ability.
3.2 Data Creation for EvalQA
Completing the VQA assessment often requires fine-graing and deep visual understanding. As emphasized in Sec. 1, the ability to assess is often overlooked yet crucial in MLLM training. To address this gap, we introduce a new benchmark, EvalQABench, to address the problem of assessing visual question-answering data. Moreover, instead of merely labeling each VQA pair with "Yes/No", we advocate for integrating feedback into each instance, an important aspect rarely seen in prior multimodal benchmarks. We consider training the model not only to assess the correctness of the answer but also to provide reasonable feedback that would increase the capability for multimodal understanding. EvalQABench comprises three datasets: training, validation, and test sets. As illustrated in Tab. 2, we present examples of the training set from EvalQABench across various question types.
MLLM-based Negative Answer Generation. The main challenge of EvalQABench lies in con- structing negative answers. When dealing with large-scale ground-truth VQA pairs, how can we automatically produce the negative answer? One viable solution to leverage a multimodal model for this purpose. Recognizing that Fuyu-8B [4] is an open-source free MLLM that stands out with the exceptional ability to process high-resolution images and perform robust well on many complex tasks. We utilize it to generate negative answers with the following prompt:

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Table 2: Selected examples from BvalQABench training set, including the ground truth answer, negative answer, and feedback.
Question Types
Objjet
YewNNo
Counting
Image
What kind of flowers the picture to the left?
Question
shining?
How many vases are
Ground-truth Answer Negative Answer
punsy shows roses.
yes
Feedback
picture
No, the sun not shining.
are the picture.
Question Types
Attribute
Number
Image
Question
What color is the truck?
type tree right?
What number is written sheep?
Ground-truth Auswer
silver
cherry
Negative Answer
white
palm
No, the tree on the right is cherry tree.
The number writlen on the sheep 3.
Feedback
No, the truck silver.
Question Types
Relation
Action
Other
Image
STOP
What does the have ber back?
What does second sign say?
Question
What are people doing?
Ground-truth Auswer
backpack
motoncycling
Negative Answer
jacket
riding hikes
stop
Feedback
No, the woman has a backpack her back.
No, the peuple the picture molorcycling.
No, the second sign says "all war"
This is the question: <Q> Please give me the wrong answer to this question. The answer should be single word or phrase.),n
<Img>
Here, <Img> and <Q> are two placeholders for the image and question from ground truth VQA pair. The output of Fuyu-8B provides a negative answer, such as "pansy", as illustrated in Fig.
Manual Filtering and Error Correction. Acknowledging that the Fuyu-8B model is not flawless and recognizing that no multimodal model, including GPT-4V, is perfect, we have implemented both manual filtering and error corrections, as illustrated in Fig. 3 for the post-data processing. Through empirical analysis, we identified 4 primary types of errors. For instance, an answer generated by Fuyu-8B may be present in the question but lacks semantic relevance or is identical to the correct answer. Additionally, some incorrect answers may result from misunderstanding the question's category, as exemplified by the example in Fig. 3. Beyond filtering, we propose error corrections for two types of questions: "Yes/No" and "Counting". For "Yes/No" questions, we directly substitute an incorrect answer with "Yes". For "Counting" questions, we first verify if the English numeral matches the correct answer; if not, we replace with random number. After applying the above filtering and correction processes, found that most of the incorrect samples had been removed.
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VQA
Question: What kind of fowerrs are the picture to the left?
EvalQA with feedback
Negative Answer
Answer:
MLLM (Fuyu-8B)
Question: Whet kind of flowers on the picture to the left?
Manual Filtering
LLM (Llama 2)
Question: What kind of flowerr the
left?
Positive
Negative answer: party
Nugativa panny
Feeelback: No, pictura showa roses.
Figure 2: Illustration of the proposed pipeline for generating negative answers and feedback.
LLM-based Feedback Generation. With the candidate's negative answer, we then focus on generating error feedback. We consider the feedback describing the reason for incorrectness will help the MLLM obtain a deeper understanding. We thus utilize the LLM Llama 2 [82] to generate the feedback by reasoning the ground truth question-answer pairs with the following prompt:
Please rephrase the question and answer:
<Q> n
<A> into one short description.
After processing by Llama 2, can get feedback like "No, the left of the picture shows roses.". Moreover, we use similar manual filtering strategies that are used the negative answer generation step remove the noisy samples with wrong formats empty output.
In summary, we start by randomly selecting 100,000 samples from 443,758 annotated VQA pairs in the VQAv2 training set [26] to generate negative answers. After manual filtering, this number is reduced to 61,094 samples. We then generate feedback for each sample and further filter out those with incorrect formats, resulting in final set of 41,592 samples. For the training set of EvalQABench, we create a one- one-positive positive-one--neaatie format by randomly selecting 32,000 negative samples from the 41,592 filtered samples, yielding total of 64,000 training data points. For the validation and test subsets, we follow a similar sampling procedure. We randomly select 100,000 samples from the VQAv2 validation set, resulting in 41,907 negative samples. From these, randomly select 2,500 negative samples each for the validation set and the test set.
3.3 Model Architecture
In this subsection, we introduce the model architecture of LOVA This model is built upon the prevalent MLLM LLa [42] with three key components: Vision Encoder, MLP Adapter, and Large Language Model. For the vision encoder, we follow LLaVA-115 [42] and implement it with pre-trained CLIp-Large vision encoder [64] with resolution 336 336. For the large language model, we adopt the widely used instruction fine-tuned model, Vicuna-75 [15]. Following [42], the MLP adapter is simple two-layer MLP since such simple design is better for reserving the visual information while achieving running efficiency. In this study, we leverage LLaVA-115 to build upon because of its exceptional performance and highly reproducible training and validating codes. Other outstanding MI.MMs, such as CogVLM [84] and Qwen-VI VI. [3], are pre-trained on billions-scale datasets or in-house datasets. This scale of data makes the training process difficult replicate and poses challenges in incorporating our proposed training tasks, GenQA and EvalQA.
3.4 Training
For brevity, we denote the LOVA3 model as FM. Given an image X,, our target is to enforce FM to generate the response Xx:
XR FM
(1)
where Xx represents the input text. Xy can be an example of the three types: 1) VQA data, e.g.. "What color is the pot?"; 2) GenQA data like "Can you provide a concise question and answer based on the image?": 3) EvalQA data, such as "What kind of flowers are on the picture to the ? nAnsser: pansy. 'PPlease examine the correctness of this question and answer according to the image content. Output Yes or No with the feedback". Accordingly, the input instruction template can be unified into the following ones:

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Manual Filtering
Manual Correction
Answer appear in the question
Different answer type
Convert the text to yes/no
"question "Wiort the con lying on??
"question" this litle boy's or ittle gitts room??
building
"wnsowe"" "rable"
"arawer "bay"
"hoyu arsowwe
Thyyu anseooe "noo
"now"
Repeat the ground truth snswer
regresentation Issue
Convert teat number
"question"" "Who in
"question" mony sinks??
"quartion" "Vinet number
photo!" "artowee" "manfy
stor??
"luy_arrswee" "raan"
"Ryy
"Myu www.ennwwerttt "teo" "oop"--"2""
"urawerr 1112
Figure 3: Examples from the manual filtering and error correction process. Red text indicates crror answers, while Green text represents manually corrected answers.
<>>USER: X, X7' \ ASSISTANT: Xn 

We follow previous MLLMs|42, 17], and design the training objective in autoregressive manner:
max logp(XR|Xx X1) =II L
p Xx,
where is the current prediction token and L denotes the response sequence length. 0 denotes the trainable parameters.
Experiments
4.1 Datasets and Settings
Training Datasets. For the fair comparison, we utilize the 665K instruction-follooinn dataset introduced in LLaVA1.5, combined with the 842K GenQA data as outlined in Tab. 1, and an additional 64K data comprising one- cooe-oostive-one-nee neg-atavv pairs as described in Section 3.2, totaling our training datasets with 1.5M samples. important to note that the datasets and annotations used in both VQA and GenQA the same. There
additional datasets involved, thus avoiding unfair comparisons caused by the introduction of new instruction data. For EvaIQA, we adopt VQAv2 to build the training set, which is already included in the original 665K instruction dataset.
Validation Datasets. We assess LOVA on widely used multimodal datasets and benchmarks. (1) VQAv2 [26] and GQA [30] are two large-scale annotated VQA datasets comprising 430K and 943K instances.
(2) VizWiz [27] challenging dataset comprising 8000 instances of test-dev set. Most of the images in this dataset are blurred, making it difficult to respond. (3) ScienceQA [50] a benchmark comprising 21k multimodal multiple-choice questions with diverse science topics. (4) POPE [39] is benchmark for evaluating the object hallucination in the MLLM. (5) MME [19], SEED-Bench [36], MMBench [45], LLaVA-Bench [43], MM-Vet [92] are five prominent multimodal benchmarks designed evaluate various capabilities of MLLMs, including object existence, color recognition, counting, OCR, etc.
Competitors. We compare LOVA3 with other SOTA models inlcuding MiniGPT-4 [101], BLIP2 [37], InstructBLIP [17], mPLUG-owl [89], LLAMA-AdapterV> [22] and LLaVA-1.5 [42]. We report the results from their paper or the benchmark leaderboard.
Implementation Details. To ensure fair comparison, we train the LOVA model without tuning any hyperparameters of LLLVA- [42] from its original supervised finetuing stage. The model is trained for one epoch across three tasks: VQA, GenQA, and EvalQA. Specifically, we employ the Adam W [46] optimizer with a learning rate of 2 x 10 and total batch size of 128. The training process takes 24.5 hours on an 8 Nvidia A100 (40G) GPU setup. Moreover, we also replace the LLM from Vicuna-7B[[s to Phi-1.5B[41] to evaluate smaller LLMs. We train VA- 1.5 and LOVA --55B by using the same training recipe. The only difference of training with Phi- that we increase the learning rate from 2 x 10 to 4 10-5 to ensure the higher performance. The model I.LVA-PPi-L trained with the original 665K VQA instruction data as the baseline. The model 3 is trained with our proposed 1.5M mixture data including VQA, GenQA, EvalQA data.
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Table 3: Results on five generic tasks including VQAv2 [26], GQA [30], VizWiz [27], Science [50], and POPE [39]. The first two columns represent the results on held-in datasets marked as ", and the last three columns represent the held-out datasets. The best result on each subtask bolded.
VQAv2 lest-dev
GQA test
VizWiz lest-dev
ScienceQA img
POPE
Method
Train Paradigm
LLM
avg
LLaVA-Phi-I.5142
VQA
Phi- 1.5B
73.2*
56.1"
33.8
57.3
87.6
LOVA (ours)
VQA, GenQA, EvalQA
Phi-1.5B
75.8
58.6"
37.24
57.8
86.0
BLIP-2 [37]
VQA
Vicuna-I Micuna-13B
41.0
41.3
19.6
61.0
85.3
InstructBLIP [17]
VQA. VQG
Vicuna-7B
49.2
34.5
60.5
InstructBLIP [17]
VQA, VQG
Vicuna-13B 13B
49.5
33.4
63.1
78.9
IDEFICS-9B [35]
VQA
LlamA-7B
50,9
38.4
35,5
44.2
[w [3]
VQA
Qwen-7B
78.8*
59.3"
35.2
67.1
LLaVA-1.5 [42]
VQA
Vicuna-7B
78.5*
62.0"
50.0
66.8
85.9
LOVA (7B(ur)
VQA, GenQA, EvalQA
Vicuna-7B
80.3
63.3
11.3
53.643.6
68.0
87.4.1.5
Table 4: Results on multimodal benchmarks, including MME [19] and SEED-Bench [36], MM-
Bench [45] and LLava-Bench [43]
SEED-Bench Image
MMBeach
LLaVA-Bench All
Method
Train Paradigm
LLM
MME
On
LLaVA-Phi-L15 [42]
VOA
Phi-1.5B Phi-1.58
1114.7
$8.2
53.7
4.1
59.0
L0VA (ours)
VQA, GengA, EvaiQA
1212.9 2292.
60.1
$5.9...
10.4.6.3
59.1.0.1
BLIP-2 [37]
VQA
Vicuna-138
1293.8
49.7
38.1
InstructBIIIP [17]
VOA, VQG
Vicuma:BB
58.8
36.0
23.7
60.9
InstructBLIPP [17]
VQA, VQG
Meuna-138
1212.8
58.2
mPLUUGowl [89]
VOA
Laama
967.3
37.9
LLaMA- 2 [22]
VQA
Llama-7B
972.7
35.2
41.0
LLaVA-1.5 [42]
VQA
Vicuna-7B
1510.7
66.2
64.3
58.3
64.0
LOVA"-78 (ours)
VOA. GeNQA, EvalQA
Vicuna-7B
1552.7+42
67.1.09
66.8
60.5.2.2
68.3.43
4.2 Main Results
Generic tasks. As shown in Tab. 3. LOVA outperforms VLa across all five datasets and obtains 3.6% improvement on VizWiz dataset, 1.3% improvement on GQA, 1.8% improvement on VQAv2 (1,932 samples are correctly predicted), and 1.2% improvement on ScienceQA. As for the object hallucination benchmarks, our model attains 87.4% accuracy at an average of its three subsets. Remarkably, these enhancements in VQAv2 and GQA performance are achieved without any extra datasets, underscoring the significant impact of integrating GenQA and EvalQA into our training to promote performance improvements on these generic VQA tasks. Based on the results from smaller L.Mss, our LOVA 3_ -1.5B outperforms the baseline LLLAVA-Ph--1. on VQAv2, GQA, VizWiz, and ScienceQA by 2.6%, 2.5%. 3.4%, and 0.5%. respectively. This demonstrates consistent improvement when training with our LOVA framework across varying LLM sizes. Additionally, a comparison of improvements for both 7B and Phi-15B on the VizWiz dataset highlights the advantage of our LOVA3 framework for this VQA task.
MME, SEED-Bench, MMBench, LLa VA-Bech. In Tab. 4, we evaluate four prevalent multimodal benchmarks, where our LOVA 3- surpasses LL155 with 42.0% on MME benchmark, 0.9% increase in accuracy on SEED-Bendh 2.5% on MMBench (En), 2.2% MMBench (Cn) and 4.3% on LLaVA-Bench. Such results showcase enhanced multimodal reasoning capabilities for complex tasks compared to vanilla LLaVA 1.5, which is solely trained with VQA tasks. By investigating the results produced by - on these multimodal benchmarks, one can see greater improvements in the smaller models. Notably, -OVA3-1.51 achieves an impressive 98.2% improvement on the MME benchmark. The lower results of LLa VA-Phi 1.5 and LOVA 3 1.5B on MMBench (Cn) may be attributed to lack of Chinese training data in the Phi-1.5 training process.
MM-Vet. In Tab. 5, we compare 3 with other approaches on MM- Vet, which is a chal- lenging benchmark including numerous complex VQA samples that demand integration of several multimodal capabilities for answering. As illustrated in Tab. 5, the results show that our -OVA3-7B outperforms 1L. by 4.0% at an average. Such improvement demonstrates the effectiveness of 1.OAA3775 7B in solving these challenging multimodal questions. Based on the results on 1OVA and VAa Phi- 1.5, one can greater improvement than 3
8

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Table 5: Multimodal reasoning ability MM-Vet [92]. Rec denotes Recognition; Know denotes
knowledge; Gen denotes Language generation; and Spat denotes Spatial awareness.
Method
Train Paradigm
LLM
Rec
OCR
Know
Gen
Spat
Total
LLaVA-Phi--L5 [42]
VQA
Phi-1.5B
22.2
L0VA (ours)
VQA, GenQA, EvalQA
Phi-1.5B
28.1
MiniGPT-4 [101]
VQA
Vicuna-7B
27.4
15.0
12.8
13.9
20.3
22.1
BLIP-2 [37]
VQ1
Vieuna-13B
27.5
11.1
11.8
7.0
16.2
22.1
InstruciBLIP [17]
VQA. VQG
Vicuna-7B
32.4
14.6
16.5
18.2
18.6
26.2
InstructBLIP [17]
VQA, VQG
Vicuna-1 13B
30.8
16.0
9.8
9.0
21.1
25.6
LLaVA-1.5 [42)
VQ1
Vicuna-7B
37.0
21.0
17.6
20.4
24.9
31.2
LOVA (ours)
VQA. GenQA, EvaIQA
Vicuna-7B
41.544.5
23,6.2.6
23.946.3
24.6
30.3
35.24-4.0
Table 6: Abaltion studies different finetuning datasets. The model is LOVA 3-BB
Finetuning Corpus
Row
GenQA-Genena Generic GenQA-Grrunding EvalQA
GQA
VizWiz ScienceQA
POPE
MME
Size
LLaVA-1.5 1.5 (Baseline)
62.0
50.0
66.8
85.9
1510.7
665K
63.1
53.1
67.4
86.9
1550.7
722K
62.8
50.9
66.4
1495.8
120K
62.8
49.1
67.8
87.0
1535.6
64K
4
/
63.3
53.2
67.4
86.7
1523.6
842K
63.7
54.4
67.0
86.9
1.50088
786K
63.1
51.1
67.5
1478.7
184K
63.3
53.6
68.0
87.4
1552.7
906K
Table Results on 10 multimodal datasets. The 64K training data for EvalQA task is generated by the Gemini-1.5-Flass model.
VQAv2
GQA
VizWiz
ScienceQA img
POPE
SEED
MMBench LLaVA
MM-Vet Total
Method
L.M
MMB
lest-dev
test
test-dev
avg
Image
En
Cn
LLaVA-Phi-L.5 [42]
Phi-1.5B
73.2"
56.1"
33.8
57.3
87.6
1114.7
58.2
53.7
4.1
59.0
22.2
1.VA (ours)
Phi-1.58
75.8
58.4"
36.9
57.8
85.8
1202.9
60.5
55.5
7.82
60.0
25.1
LLaVA-1.5 [42]
Vicuna-7B
78.5*
62.0"
50.0
66.8
85.9
1510.7
66.2
64,3
58.3
64.0
31.2
LOVA--7B (ours)
Vicuna-7B
80.3"
63.4"
54.2
70.8
85.6
1526.8
67.6
66.5
57.6
67.7
32,2
4.3
Ablation Study
We split the data used in the GenQA task into two groups: GenQA-Gened and GenQA-Grounding The findings, presented in Tab. 6, are instrumental investigating the contributions of GenQA and EvalQA to model efficacy. (1) Comparing the first four rows, one can find that both GenQA-Genee and EvalQA data are more effective in improving performance than Manno--Grunding... (2) By comparing rows 4 and 7, it demonstrates the effectiveness of EvalQA across five datasets, especially on MME. (3) When comparing rows 6 and 7, by removing GenQA
from the finetuning corpus, the performance drops significantly on MME and VizWiz. (4) Compare the rows O and 3. one can observe that even adding 64K data into the training, there are obvious improvements in GQA, ScienceQA, and MME. By analyzing the data size, we did not introduce any new datasets for training the GenQA task. For EvaIQA, we only added 32K new negative answer annotations while retaining the original questions used for training VQA capabilities. The details of the data size are provided in the right column Tab. 6.
4.4 Training with Gemini-Generater EvalQA Data
Rather than using the open-source model Fuyu-8B [4] to create the training data for EvalQABench. we also explore the use of the commercial model Gemini-1.5-Fllase
both the MLLM and LLM in Fig. 2 to generate negative answers and one-sentence feedback. The experimental results, presented in Tab. 7, indicate that regardless of whether we use the open-source model Fuyu-8B the commercial model Gemini- 1.5-Flash, our proposed training paradigm LOVA3 consistently improves performance across both smaller and larger bascline models.

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Table 8: Results of multimodal large language models on the test set of EvalQA (ours).
Test
Method
LLM
Accuracy
Precision
FI Score
(%)
Vision Language Pretraining Model
BLIP2 [37]
Flan-T5-XXIL
58.00
82.79
32.47
87.80
Multimodal Large Language Models
InstructBLIP [17]
Vicuna-7B
38.04
41.49
48:47
29.76
InstructBLIP [17]
Vicuna-135 13B
61.42
$7.60
69.18
24.82
CogVLM [84]
Vicuna-7B
60.64
$6.59
69.88
19.32
Qwen-VI. VL--hat [3]
Qwen-7B
63,66
63.48
63,90
49.34
M [95]
InternLM-7B
69.58
70.66
68.76
52.62
LLaVA-1.5 [42]
Vicuna-7B 7B
64.92
61.28
69.80
23.84
LOVA (ours)
Vicuna-7BB 7B
79.58
79.15
79.72,9.92
49.26
4.5 Benchmark of EvalQABench
We report the evaluation results on our EvalQABench
in Tab. 8 to validate the EvalQA ability of current SOTA models and LOVA We select BLIP2 [37], InstructBLIP [17], CogVLM [84]. Qwen-VL- [3], InternI.MXX. [95], and LL1 [42] for the comparison. We ask these models to answer "Ys"" or "No" strictly and record the results for calculating the Accuracy, Precision, F1 score, and No (%) metrics. Here, No (%) indicates the percentage of results classified "No," which ideally should approximate 50% due to the one-positive-one-negaaii setting utilized in test set. As indicated by the data presented in the table, BLIP2 predominantly yields "No" responses across most test instances. Among the state-of-the-art MLLMs, InternLM-XC stands out by delivering superior performance on these four metrics. Trained with EvalQA data, LOVA shows several improvements our baseline LL. by margins of 14.66%, 17.87%, and 9.92% in Accuracy, Precision, and Fl Score, respectively.
5 Conclusion and Limitations
In this work, we propose novel multimodal framework, LOVA3, which is capable of mimicking the human visual question answering, asking, and assessment to achieve deeper multimodal under- standing. We introduce two additional training tasks, GenQA and EvalQA, to help MLLM acquire these abilities. We establish EvalQA novel benchmark to assess the VQA samples between multiple MLLMs. Experimental results show that LOVA achieves superior performance across various benchmarks, including MM-Vel SEED, and VizWiz, demonstrating the effectiveness of the two additional abilities.
Limitations. (1) Due to computational constraints. we do not test larger LLMs, such as the 13B or 34B variants. However, we believe that our L.VAA3 could be beneficial for larger LLMs,, as other MILMMs have shown performance improvements with increased L.LM scale. (2) GenQA and EvalQA as two additional tasks increase training costs, but is inevitable for an MILLM to acquire new capabilities. (3) Due to the limited scope of instruction tuning datasets, LOVA3 3 cannot address domain-specific multimodal tasks well, such as text-centric VQA or mathematic-relevant VQA.
6 Acknowledgement
This research is supported by National Research Foundation, Singapore and A *STAR, under its RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) grant call (Grant No. 12001E0059) - SIA-NUS Digital Aviation Corp Lab. Mike Zheng Shou supported by the National Research Foundation, Singapore under its NRFF Award NRF-NNFFF3-2021-- 20211-0008 Pan Zhou was supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier and
grants (project ID:
10

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References
[1] Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruy Batra, Devi Parikh, Stefan Lee, and Peter Anderson. nocaps: novel object captioning at scale. In ICCV, 2019. 3
[2] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 3
[3] Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding. localization, text reading, and beyond. arXiv preprint arXiv:2308. /2966, 2023. 2, 3, 6, 8, 10
[4] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Taşırlar. Introducing our multimodal models, 2023. 2, 4,
[5] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS, 2020. 3
[6] Georg Cantor. Über unendliche, lineare Punkkmannggfaliggketnn:: Arbeiten zur Mengenlehre aus den Juhren 1872-1884, volume 2. Springer- Springer-Verlag. 2013.
[7] Junbum Cha, Wooyoung Kang, Jonghwan Mun, and Byungseok Roh. Honeybee: Locality- enhanced projector for multimodal 1lm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 3
[8] Chi Chen, Ruoyu Qin, Fuwen Luo, Xiaoyue Mi, Peng Li, Maosong Sun, and Yang Liu. Position-enhancee visual instruction tuning for multimodal large language models. arXiv preprint S.KIv:2208.13337 2023.
[9] Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, and Mohamed Elhoseiny. Minigpt-v2: large language model unified interface for vision- vision-language multi-task learning. arXiv preprint arXiv:2310.09478 2023.
[10] Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal 1im's referential dialogue magic. arXiv:2306.15199 2023. 3, 18
[11) Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang. Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, and Feng Zhao. Are we on the right way for evaluating large vision-language models?, 2024. 3
[12] Xinlei Chen, Hao Fang. Tsung-Yr Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv:1504.006 00324,002550000 2015. 3
[13] Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, and Ming- Wei Chang. Can pre-trained vision and language models answer visual information-seeking questions? arXiv preprint 2rX.v:2302.11713 2023.
[14] Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma. Ma, Jiaqi Wang. Xiaoyi Dong, Hang Yan, Hewei Guo, Conghui He, Botian Shi, Zhenjiang Jin, Chao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Pinlong Cai. Licheng Wen, Xiangchao Yan, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites, 2024.
[15] Wei-Lin Chiang. Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, I Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric Xing. Vieuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023. 3, 6,
11

---

[16] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghami, Siddhartha Brahma, et al. Scaling catruction-inettune language models. arXiv preprint arXiv:2210. 1416, 2022. 3
[17] Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. Instructblip: Towards general-purpose Vision-language models with instruction tuning. 2023. 2, 3, 7, 8, 9, 10
[18] Zhihao Fan, Zhongyu Wei, Siyuan Wang. Yang Liu, and Xuan-Jing Huang. A reinforcement learning framework for natural question generation using bi-discriminators. In Proceedings of the 27th International Conference on Computational Linguisties, pages 1763-1774, 2018. 3
[19] Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, al. Mme: A comprchensive evaluation benchmark for multimodal large language models. arXiv:2306.13394 2023. 2, 3, 8
[20] Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang. Xudong Lin, Dan Roth, Noah Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive. arXiv preprint arXiv:2404. 12390, 2024. 3
[21] Richard Gale. Asking questions that matter... asking questions of value. International Journal for the Scholarship of teaching and learning, 3(2):3, 2009.
[22] Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al. Llama-adapter v2: Parameter-efficient visual instruction model. arXiv:2304. 15010, 2023. 3, 7,
[23] Peng Gao, Renrui Zhang, Chris Liu, Longtian Qiu, Siyuan Huang, Weifeng Lin, Shitian Zhao, Shijie Geng, Ziyi Lin, Peng Jin, Kaipeng Zhang, Wenqi Shao, Chao Xu, Conghui He, Junjun He, Hao Shao, Pan Lu, Hongsheng Li, and Yu Qiao. Sphinx-x: Scaling data and parameters for family of multi- large language models, 2024. 3
[24] Timin Gao, Peixian Chen, Mengdan Zhang, Chaoyou Fu, Yunhang Shen, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Xing Sun, Liujuan Cao, and Rongrong Ji. Cantor: Inspiring multimodal chain-of-thought of mllm, 2024. 3
[25] Brian Gordon, Yonatan Bitton, Yonatan Shafir. Roopal Garg, Xi Chen, Dani Lischinski, Daniel Cohen-Or, and Idan Szpektor. Mismatch quest: Visual and textual feedback for image-text misalignment. arXiv preprint arXiv:2312.03766 2023. 3
[26] Yash Goyal, Tejas Khot, Douglas Summers-Stap Stay,, Dhruv Batra, and Devi Parikh. Making the in vqa matter: Elevating the role of image understanding in visual question answering. In CVPR, 2017. 1, 2, 3, 4, 6, 7, 8, 17, 18, 19
[27] Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham. Vizwiz grand challenge: Answering visual questions from blind people. In CVPR, 2018. 1, 2, 7,8
[28] Muyang He, Yexin Liu, Boya Wu, Jianhao Yuan, Yueze Wang, Tiejun Huang, and Bo Zhao Efficient multimodal learning from data-centric perspective. arXiv preprint 1533 2024. 3
[29] Shaohan Huang. Li Dong, Wenhui Wang. Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, et al. Language is not all you need: Aligning perception with language models. arXiv:2302. /4045, 2023. 3
[30] Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In CVPR, 2019. 1, 3, 4, 7, 8, 17, 18
[31] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In EMNLP, 2014. 4, 18
[32] Ranjay Krishna, Michael Bernstein, and Li Fei-Fei. Information maximizing visual question generation. Proceedings of the IEFF/CVE Conference on Computer Vision and Pattern Recognition, pages 2008-2018 2019.
12

---

[33] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJC,, 2017. 4, 18
[34] Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reasoning segmentation via large language model. 2024. 3
[35] Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M Rush, Douwe Kiela, et al. Obelics: An open web-scale filtered dataset of interleaved image-text documents. In Thirty- seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023. 8
[36] Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal iims with generative comprehension, 2023. 3, 7, 8, 18
[37] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-iman pre-training with frozen image encoders and large language models. arXiv:2301. 12597, 2023.
3, 8, 9, 10
[38] Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, and Jiaya Jia. Mini-gemini: Mining the potential of multi-modality vision language models. arXiv preprint arXiv:2403.18814, 2024. 3
[39] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination large vision-language models. arXiv:2305.10355 2023. 3, 7,8
[40] Yikang Li, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang. Xiaogang Wang, and Ming Zhou. Visual question generation as dual task of visual question answering. In Proceedings the IEEE conference on computer vision and pattern recognition, pages 61 61--612, 2018. 2, 3,
[41] Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giomo, Suriya Gunasekar, and Yin Tat Lee. Textbooks are all you need ii: phi-1.5 technical report. arXiv preprint arXiv:2309.05466. 2023. 7
[42] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023. 1, 2, 3, 6, 7, 8, 9, 10, 17
[43] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS, 2023. 1, 3, 4, 7, 7, 18
[44] Mengchen Liu, Chongyan Chen, and Danna Gurari. An evaluation of gpt-4v and gemini in online vqa, 2024.
[45] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangho Zhao Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: is your multi-modal model an all-around player? arXiv preprint arXiv:2307.0628/ 2023. 3, 8, 18
[46] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
[47] Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li. Yaofeng Sun, et al. Deepseck-vl: towards real-world vision-language understanding, arXiv preprint arXiv:2403.05525. 2024. 3
[48] Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, and Aniruddha Kembhavi. Unified-io 2: Scaling autoregressive multimodal models with vision, language, audio, and action. 2024.
[49] Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang. Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. In The Twelfth International Conference on Learning Representations, 2024. 3
13

---

[50] Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai- Wei Chang, Song- Bang-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. NeurIPS, 2022. 3, 7, 8, 18
[51] Gen Luo, Yiyi Zhou, Tianhe Ren, Shengxin Chen, Xiaoshuai Sun, and Rongrong Ji. Cheap and quick: Efficient vision-language instruction tuning for large language models. 2023. 3
[52] Arjun Mani, Nobline Yoo, Will Hinthom, and Olga Russakovsky. Point and ask: Incorporating pointing into visual question answering. arXiv preprint arXiv:2011.13681, 2020. 17
[53] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and comprchension of unambiguous object descriptions. In CVPR, 2016. 18
[54] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-vqa: A visual question answering benchmark requiring external knowledge. In CVPR, 2019. 1, 3, 18
[55] Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa document images. In WACV, 2021.
[56] Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty. Ocr-vqa: Visual question answering by reading text in images. In ICDAR, 2019. 3, 4, 17, 18
[57] Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, and Laurens Van Der Maaten. Learning by asking questions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11 11-20, 2018.
[58] Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, et al. Anymal: An efficient and scalable any-modality augmented language model. arXiv preprint N.Nv:2330.16055 2023. 3
[59] Issey Masuda Mora, Santiago Pascual de la Puente, and X Giro-i Nieto. Towards automatic generation of question answer pairs from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1-2, 2016. 3
[60] Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende. Generating natural questions about an image. arXiv preprint arXiv: /633vv/603.0005)
2016. 3
[61] OpenAL. Gpt-4 technical report, 2023.
[62] Zhiliang Peng, Wenhui Wang. Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Grounding multimodal large language models the world. arXiv:2306. 14824, 2023. 3
[63] Bryan A. Plummer, Liwei Wang, Christopher M. Cervantes, Juan C. Caicedo, J. Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer sange-to-sentene models. International Journal of Computer Vision, 123:74 93, 2015, 3
[64] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 6
[65] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models unsupervised multitask learners. OpenAl blog, 2019. 3
[66] Hanoona Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Erix Xing, Ming-Hsuan Yang, and Fahad S. Khan. Glamm: Pixel grounding large multimodal model. In CVPR, 2024. 3
[67] Jie Ren, Yao Zhao, Tu Vu, Peter Liu, and Balaji Lakshminarayynaan Self-evaluation improves selective generation in large language models. arXiv preprint arXiv:2312.09300 2023. 1
14

---

[68] Shuhuai Ren, Linli Yao. Shicheng Li, Xu Sun, and Lu Hou. Timechat: A time-sensitive multimodal large language model for long video understanding. In CVPR, 2024.
[69] Azzurra Ruggeri and Tania Lombrozo. Learning by asking: How children ask questions to achieve efficient search. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 36, 2014.
[70] Claude Sammut and Ranan B Banerji. Learning concepts by asking questions. Machine learning: An artificial intelligence approach, 2:167-192, 1986.
[71] Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. A-okvqa: benchmark for visual question answering using world knowledge. arXiv, 2022. 3, 4, 18
[72] Thomas Scialom, Patrick Bordes, Paul-Alexis Dray, Jacopo Staiano, and Patrick Galli- nari. What bert sees: Cross-modal transfer for visual question generation. arXiv preprint arXiv:2002. 10832, 2020. 3
[73] Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh. Cycle-consistency for robust visual question answering. Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages
2019, 3,4
[74] ShareGPT. https: httts:///sarrgpt.coo//o 2023. 18
[75] Oleksii Sidorov, Ronghang Hu, Mareus Rohrbach, and Amanpreet Singh. Textcaps: a dataset for image captioning with reading comprehension. ECCV, 2020. 3, 18
[76] Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), June 2019, 3, 20
[77] Dingjie Song. Shunian Chen, Guiming Hardy Chen, Fei Yu, Xiang Wan, and Benyou Wang. Milebench: Benchmarking milms in long context, 2024. 3
[78] Jingqun Tang, Chunhui Lin, Zhen Zhao, Shu Wei, Binghong Wu, Qi Liu, Hao Feng, Yang Li, Siqi Wang, Lei Liao, Wei Shi, Yuliang Liu, Hao Liu, Yuan Xie, Xiang Bai, and Can Huang. Textsquare: Scaling up text-centric visual instruction tuning, 2024. 3
[79] Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models, 2024. 3
[80] Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? exploring the visual shortcomings of multimodal llms. In CVPR, 2024. 3
[81] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv:2302. 13971, 2023.
[82] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Ilama 2: Open foundation and fine-tuned chat models. arXiv preprint urXiv:2307.09226. 2023.
[83] Nihir Vedd, Zixu Wang, Marek Rei, Yishu Miao, and Lucia Specia. Guiding visual question generation. arXiv preprini arXiv:21 2021. 3
[84] Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, and Jie Tang, Cogvlm: Visual expert for pretrained language models. 2023. 2, 3, 10
[85] Wenhai Wang. Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, al. Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. arXiv preprint arXiv:2305.1117. 2023. 3
[86] Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, James Xu Zhao, Min- Yen Kan, Junxian He, and Michael Xie. Self-evaluation guided beam search for reasoning. Advances in Neural Informa- tion Processing Systems, 36, 2024.
15

---

[87] Xing Xu, Tan Wang, Yang Yang, Alan Hanjalic, and Heng Tao Shen. Radial graph convo- lutional network for visual question generation. IEEE transactions on neural networks and learning systems, 32(4))1654-1666) 2020. 3
[88] Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al. mplug-owl: Modularization empowers large language models with multimodality. arXhv:204.14178, 2023. 3
[89] Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, Chaoya Jiang, Chenliang Li, Yuanhong Xu, Hehong Chen, Junfeng Tian, Qian Qi, Ji Zhang, and Fei Huang. mplug-owl: Modularization empowers large language models with multimodality,, 2023. 7,8
[90] Haoxuan You, Haotian Zhang, Zhe Gan, Xianzhi Du, Bowen Zhang, Zirui Wang, Liangliang Cao, Shih-Fu Chang. and Yinfei Yang. Ferret: Refer and ground anything anywhere at any granularity. In The Twelfth International Conference on Learning Representations, 2024. 3
[91] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metries for semantic inference over event descriptions. ACL, 2014.
[92] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabili- ties. arXiv preprint arviv:2308. 02490, 2023. 2, 3, 7, 9, 20
[93] Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang. Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng. Zhenzhu Yang. Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of CVPR, 2024. 3, 18
[94] Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 6720-6731, 2019. 3
[95] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Shuangrui Ding, Songyang Zhang. Haodong Duan, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. Internin-xcomposer:: A vision-language large model for advanced text-image comprehension and composition. arXiv preprint 2rXiv:2309.1517 2023. 2, 10
[96] Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wet Chang, Peng Gao, and Hongsheng Li. Mathverse: Does your multi- modal Ilm truly see the diagrams in visual math problems? arXiv preprint arXiv:2403. 14624, 2024. 3
[97] Shijie Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, and Jiawan Zhang. Automatic generation of grounded visual questions. arXiv preprint arXiv:1612.06530 2016. 3
[98] Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv:2205.0
2022. 3
[99] Yizhe Zhang, He Bai, Ruixiang Zhang, Jiatao Gu, Shuangfei Zhai, Josh Susskind, and Navdeep Jaitly. How far are we from intelligent visual deductive reasoning?, 2024. 3
[100] Henry Hengyuan Zhao, Pan Zhou, and Mike Zheng Shou. Genixer: Empowering multimodal large language models as a powerful data generator. arXiv preprint arX1v:2312.06771,, 2023.
[101] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhanc- ing dision-language understanding with advanced large language models. 2045022304.11552 2023. 3, 7,
16

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A Broader Impacts
In this paper, we propose new training framework for imbuing two essential abilities into the model training. We also propose the EvalQA task with a new benchmark of 64,000 training data and 5,000 validation and testing data. In summary, this work would inspire future work pay more attention visual question asking and assessment. For these two tasks, there still exists some space for involving more GenQA tasks and more formulations EvalQA.
B Additional Details of Implementation
665K instruction tuning data. We follow the backbone MLLM LLa to adopt the 665K esstuction-follovinng data into the supervise finetuning stage. We present the details of the 665K instruction data in Tab. 9 for convenient browsing. The details include the dataset name, size, and instruction prompts.
The baseline model LL. [42] includes two stages: image-text alignment pertaining stage and supervised instruction tuning stage. The first stage involves only the image caption datasets for aligning two modalities by only finetuming the two-layer MLP adapter. In this paper, we are investigating the effectiivness of two additional tasks in the supervised instruction tuning stage. Thus, we use the pretraining weights of the first stage for fair comparison and train the model in Fig. 4. The model comprises three key components: Vision Encoder, MLP Adapter, and Large Language Model. The vision encoder is responsible for processing the input image X1 to align learned visual features with text input Xr. Next, the Multi-Layer Perceptron (MLP) adapter projects the visual feature F, to Zı. Finally, the large language model utilizes Zr and the current text embedding Zr to predict the response in a left-to-right manner. During training, the data of the three tasks is mixed. By such joint training, MILM exhibits deeper comprehension and promising reasoning ability.
Model training.
picture
Question: the pat'esAnwer blue.
shows roses.
blue
Large Language Model
Zy
Vision Enceder
MI.P Adapter
What culor che por?
VQA
you provide question and answer based un the image?
lef?"mnnserr: pancy. YaPlezae conmine the this question and sccoeding image Ouiput or No with the feedbuck.
GenqA
EvalQA
Figure 4: Illustration of the model training of LOVA
Hyperparameter.
The hyperparameters of LOVA3 aligned with those of 5 ensure a fair comparison, as illustrated in Tab 10. The exceptional performance highlighted in Tab. 3, 4, and 5 of the main paper, achieved without any modulation of hyperparameters, demonstrates the effectiveness and robustness of our LOVA3
C Details of GenQA Data
Generic VQA. It includes four datasets: VQAv2 [26]. GQA [30], OCR-VQA [56] and Count- ing! 10K [52]. The generic VQA data type we developed focuses on enabling the model to produce basic and general QA pairs. We incorporate VQAv2 and GQA, two fundamental VQA datasets for granting LOVA3 the capability to learn how to ask questions like a human. Additionally, to increase the question diversity, we introduce two supplementary VQA tasks: counting VQA and long-response VQA. VQA.Counting 10Kt, dataset developed in-house by reformulating the original PointQA [52]
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Table 9: 665K instruction data of LLaVAI.5. The content is from LLaVA1.5 for convenient browsing.
Daraset Name
Instruction Prompts
LLaVA [43]
158K
SharoGPT
40K
VQAv2 [26]
83K
Answer the question using single word or phrase.
GQA [30]
72K
OKVQA [54]
9K
OCRVQA [56]
80K
A-OKVQA [7]]
50K
Answer with the option's 's letter from the given choices directly.
TexCCaps [75]
22K
Provide one one-seneence caption for the provided image.
Re1C0CO[31,53]
30K
Note: randomly choose between the two formats Provide short deseription this region.
[33]
Provide bounding box coordinate the region this sentence describes.
Total
665K
Table 10: Hyperparameters of LOVA are the same as the L....
Hyperparameter
Finctune
batch size
128
learning rate
2 x 10-5
learning rate schedule
cosine decay
learning rate warmup ratio
0,03
weight decay
0
epach
optimizer
Adam
DeepSpeed stage
format, contributes to the counting type data generation. Moreover, for most of the above generic VQA with short response, is necessary for MLLM to learn to ask questions with long answers. Thus, we leverage the conversation subset of (LaVA-150K [43] and then filter out overly lengthy sentences (e.g., the word number 2 200.) to yield LLLVVA-500- with almost 250K samples.
Multi-choice VQA. Apart from generic VQA, there is another variant known as multi-choice VQA. This format has become increasingly popular in recent multimodal benchmarks, including ScienceQA [50], SEED [36], MMBench [45] and MMMU [93]. Such a data format can be seen as better way evaluate the reasoning ability of MLLMS rather than the direct text response in an open-ended way. As each MC VQA sample comprises one correct answer and three incorrect yet plausible alternatives, it will introduce higher level of complexity for model learning. Thus, We proceed to make the LOVA3 3 learn to produce multi-choice VQA data.
Multi-turn VQA. It is also a complex multimodal data format. We incorporate this data type into the training of LOVA3, enabling to master the art of generating varied questions within a dialogue context from single image. Recognizing that the VQAv2 and GQA datasets offer multiple questions per image, we carefully select 83,000 and 72,000 multi-turn VQA instances from each dataset, respectively.
REC and REG. Recent studies such as Shikra [10] have recognized the importance of making MLLM talk about the image regions for asking and answering (e.g., describing the region by giving a bounding box in an image) in grounding tasks. Being able to refer to region precisely when asking or answering a question demonstrates a strong capability of multimodal reasoning. Possessing this capability would enhance an MILM's potential as an intelligent assistant in human-Al interactions by accurately identifying and referring regions of interest. Thus, besides considering the aforementioned multimodal tasks, we also consider involving grounding tasks like REC and REG to enhance the model capability related to positions. For REC, it aims to ground the region of image with the given referring expression. About REG, the target is to give the corresponding expression when giving the exact coordinates. We randomly select 30K samples from RefCOCO [31] and Visual Genome (VG) [33], respectively.
How about the asking ability of ? We provide some examples of prompting LOVA3 to generate VQA pairs as in Fig. 9. One can see that LOVA3 capable of asking versatile questions based on the content of the unlabeled images. Such results demonstrate the potential of the current MI.LM to actively ask questions. We would like this finding to inspire future works that explore human-Al interaction in depth.
18

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D EvalQABench
Why Fuyu-8B and Llama 2? To build the EvalQABench, we used two open-source models, Fuyu- 8B and Llama 2, generate negative answers and feedback, respectively. These models were chosen due to the zero financial cost of producing a training dataset. Furthermore, our empirical investigation found that Fuyu-8B and Llama 2 capable of generating the data by following the instruction prompts described in Sec. 3.2. Such results prove that GPT-4 is not necessary for our purpose.
Why does manual filtering and correction work? The reason is that the models of Fuyu-8B and Llama are two closed-formed models, which will output incorrect samples with similar patterns even set by the hyperparameters of inference mode. Therefore, we observe that use manual checking feasible and enough to remove most of the failure cases. Moreover, GPT-4 is a closed-form model yet that exhibits error patterns.
Verification of data. As mentioned in the main paper, we select 100,000 samples from annotated VQA pairs of the VQAv2 training set and then use Fuyu-8B to generate negative answers for subsequent manual filtering and error correction. We obtain 61,094 filtered samples, which is approximately 61% % accuracy in generating negative answers. After that, we prompt Llama 2 to produce feedback. In this process, we also manually filter the samples with incorrect formats and finally obtain 41,592 samples. It is almost 68% accuracy in creating feedback.
Details of each procedure. In detail, we present the amounts of each procedure in creating the EvalQABench training set in Tab. 11. Initially, we select 100,000 samples and then use Fuyu-8B to obtain 99, 99,998 valid outcomes, and then we use manual filtering to remove 38,904 samples. We conduct error correction to 14,814 samples and then pass 61, 11,094 samples to Llama 2 for feedback generation. After that, we adopt manual filtering remove 19,502 samples with incorrect formats. The filtering of feedback includes overlength output or none of output.
Table 11: Data amount details of creating EvalQABench training set.
Procedures
Amount
Raw Data
100,000
Negative answer generation
99,998
Manual filtering
61,094 (-38,904)
Error Correction
01,094 (14,814)
Feedback generation
61,094
Manual filtering
41 41,592 (-19,502)
Data distribution across categories We provide the data distribution of the nine question types across the "Object", "Yes/No", "Counting", "Color", "Number", "Attribute", "Relation", "Action", and "Others" of the EvalQABench training set in Tab. 12. One observe that there are 26.7 %% question belongs to Others. It is noted that "Others" includes versatile questions such as "What does the image represent?". "Who is not out of focus?", "What does the back of the bus say?", "What time does the clock report?", and "How old is this man?". These questions, with diverse scopes, bring diversity our EvalQABench. Due to the inherent question bias in the original VQAv2 [26] training set, the number of questions categorized as "Number" is limited. Nevertheless, it is important to note that such biases are also present real-world scenarios. We also provide the staties of the other seven question types in the table.
Data distribution of negative answers.
To analyze the data distribution of produced negative answers, we build word cloud in Fig. 5. One can observe that "Yes" and "No" are the two majority negative answers due to the higher proportion of "Yes/No" questions. While colors and numbers are also the two high lighfrreueecy word types that appeared in the negative answers.
Data distribution of feedback.
For analyzing the distribution of feedback, we dive into three aspects: sentence length of feedback, noun counts, and verb counts as in Fig. 6, 7, 8.
19

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Table 12: Statistic of question types of EvalQABench training set.
Statistic
Number
Proportion
Total Questions
32,000
20 YesRll
Object
2,418
7.55%
Oehers
Yes/No
6,804
21.26%
2B
Counting
4,880
15.25%
Color
3,756
11.73%
Counting
Attribute
343
5.67%
Number
Number
1,814
1%
1.07%
Action
Relation
2,380
7.44%
Atribute
3.99%
6.31%
Action
1,274
3.98%
Colos
45.8%
Relation 7. 7,40%
Other
8,331
26.03 %
7.615
Otjert
1
tan
oot
shadow
plain
mital
mirror
stop
computer
4
Yes
several eating
W
banana
Reed
riding
dog
kite
clock
nothing
man
che
ese
0
pizza
bedroom
doroomm
Lop
stov
Luel
Luek
play
left restaurant wane sinnkn
human
glass
zebra
g
een
refrigerator
outsi
right
lots
living
pink
taaniing
round
top
phone
arrfing
6
sky
Curr
caton
Soathroom oom Fitersson
shirt
grass
hap
apple
Uppas
straight
happy
teddy bear
noon
wood paper
box
nowngran
bus
10
plastic
food
bike
giraffe
ed
posting
skateboard
cat
O
London
a
mwart
kitchen
talking
playing
yellow
batter
broccols
No
table
young
dirut
none
baby
Figure 5: The word cloud of total negative answers.
E Failure cases of EvalQABench
In this section, we show some failure cases of our LOVA3. As shown in Tab. 13, there are three failure cases: the first case is from the TextVQA [76], and the last two cases are from MM-Vet [92]. In the first case, when the watch is rotated -90º and an incorrect OCR reference is provided, our LOVA3 fails to give the correct time. The second example highlights the current limitations of LOVA in mathematical calculations and multi-step reasoning. In the third example, LOVA fails to correctly interpret the window of the living room, resulting in an incorrect answer. It important to note that due to the limited text-centric instruction tuning datasets and mathematic relevant task-specific data the current experimental settings, LOVA falls short in handling text-centric VQA, and mathematic problem-solving. We believe these failure cases are primarily caused by the shortage of relevant instruction tuning datasets. This leaves room for future exploration, while our work focuses mainly on highlighting the importance and effectiveness of two additional high-level abilities for enhancing multimodal understanding in this paper. We believe that exploring the creation or collection of more mathematical or text-centric data for training will be essential for future work.
20

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6000
5000
4000
Count
3000
2000
1000
0
5.0
7.5
10.0
12.5
15.0
17.5
20.0
Length of Feedback
Figure 6: The distribution of the length of feedback.
Count (log scale)
woman
photo
animal
dog
cat
raom
image
sign
shirt
train
table
bus
plate
water
question
wall
tree
car
clock
number
plane
food
boy
hand
giri
harse
bird
pizza
light
truck
Nouns Category
Figure 7: The top-30 nouns of feedback.
1.20
Count (log scale)
hold
show
make
help
play
stand
know
take
locate
eat
look
see
hope
use
contain
say
ride
go
walk
fly
picture
appear
hang
read
include
call
cover
write
Verbs Category
Figure 8: The top-30 verbs of feedback.
21

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Question:
What is the dog daing in the image?
Question: What is the boy doing in the image?
Question: What is the cat doing in the image?
Answer: The boy the image playing
Answer:
The dog is layling down and resting
baseball, wearing catcher's mitt, and readly position to catch a ball.
The cat is sitting bed next to a pillow.
Answer:
head on cushion or brown pillow.
Question: How many motorcycles are there?
Question: woman the image skilin or snowboarding?
Question: frog?
boy doinng with
Answer: 4
Answer: not snowboarding.
The woman the image is skiling
He is either showing the frog to someone or is about to let the frog jump plate.
Answer:
Question: is dog doing in image?
Question: What the animal that standing in the snow called? Answer: sheep
Question: What is man wearing image?
The dog in the image jumping in the air catch frisbee its mouth.
Answer:
Answer: The man in the image is wearing blue jacket and helmet.
Figure 9: We ask the LOVA generate the diverse question-answer pairs.
22

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Table 13: Some failure cases of LOVA3.
Image:
Question:
What is the time? 'Reference OCR token: N, u, g0 \Answe the question using a single word phrase.
Answer:
10:10
Gasoline
Self Serve
3.49
Reqular
3.55
Plus
3.65
Diesel
3.60 9 No.2
Image:
Question:
How many gallons of supreme gasoline I get with $50?
Answer:
With $50, you can get approximately 110 gallons of supreme gasoline, indicated by the gas station sign in the image.
DINING AREA 9'10" 1310
10'S7
LIVING ROOM
Briweee
Image:
Question:
In which direction does the living room window face?
Answer:
The living room window faces the dining area.
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Reviews:
[{"review_id": "UpaeIyfhP0", "reviewer": "Reviewer_UkuU", "summary": "The paper introduces LOVA3, a framework designed to enhance Multimodal Large Language Models (MLLMs) by incorporating not only visual question answering (VQA) but also the capabilities of generating questions (GenQA) and evaluating question-answer pairs (EvalQA). The primary objective is to improve the comprehensive multimodal understanding of AI models. \n\nLOVA3 includes the development of EvalQABench, a benchmark with 64,000 training samples to evaluate VQA data quality. The framework uses the LLaVA-1.5 model as a base, incorporating datasets like VQAv2 and GQA to train these new tasks. Experimental results on ten multimodal benchmarks demonstrate that LOVA3 significantly improves the models' performance, highlighting the benefits of incorporating comprehensive questioning and evaluation abilities into MLLMs. The paper emphasizes the approach and robust results, despite noting the increased computational cost and the need for further testing on larger models and domain-specific tasks.", "questions": "1. How about the prompt stability of the QA generation and the differences when using the different variants of prompts?\n2. Why does the work apply Fuyu-8B instead of LLaVA 1.5 for the data generation and is there any comparison between the different new VLMs?", "limitations": "NA", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. LOVA3 introduces a strategy that extends beyond traditional VQA tasks by incorporating question generation and evaluation.\n2. The creation of EvalQABench provides a rigorous way to test and improve MLLMs.\n3. The multiple perspectives of experimental results provide insights of the proposed framework across multiple benchmarks.", "weaknesses": "1. Incorporating additional tasks like GenQA and EvalQA, but the two tasks are also the existing steps of the visual language instruction generation for visual question answering (e.g. SEED-Bench) or visual instruction tuning (e.g., LLaVa-Bench). They also used LLMs or MLLMs for the dataset generation and validation. To explained the special novelty or contribution would be better.\n2. The work doesn't provide detailed explanations on how to validate the generated data quality from humans instead of using imperfect models (LLMs or VLMs).  It uses Fuyu-8B for data generation but employs a stronger MLLM (LLaVA 1.5) as the base model for instruction tuning. Since LLaVA 1.5 is stronger than Fuyu-8B, the generated negative samples would be less challenging and easier to recognize by stronger models.\n3. The paper lacks a more in-depth analysis of potential data biases and strategies to mitigate them.\n4. The proposed benchmark is relevant to visual question answering and data generation for visual question answering. It would be necessary to survey and discuss the recent existing datasets (e.g., VQA-GEN, CrossVQA, OVQA, STAR) and generated benchmarks (e.g., LLaVA-Bench, SEED-Bench, SOK-Bench, CinePile) as fully considered.   \n5. The paper does not provide the generated dataset for review, which is important for the validation of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for thoroughly reviewing our work! We have carefully considered all your concerns and addressed them in the following responses.\n\n**W1: Comparison with SEED-Bench and LLaVA-Bench in using LLMs or MLLMs.**\n\n(1) GenQA and EvalQA are two new training tasks, whereas SEED-Bench and LLaVA-Bench are test benchmarks. For GenQA, no LLMs or MLLMs are used for data creation. We propose an efficient strategy for creating the data from existing datasets (see Appendix C). For EvalQA, we use Fuyu-8B [3] and LLaMA-2 [4] for data creation. This cannot be a weakness of our work, as creating over 32,000 samples for training the assessment ability necessitates using LLMs/MLLMs rather than human labeling. Additionally, we demonstrate the effectiveness of using non-commercial LLMs/MLLMs in data generation rather than commercial models like GPT-4(V).\n\n(2) Novelty: To the best of our knowledge, LOVA$^3$ is the first work to imbue the asking and assessment abilities in training a robust and intelligent MLLM. Additionally, we are the first to propose GenQA and EvalQA tasks for enhancing the model’s problem-solving ability. Contribution: We contribute a new training pipeline, a new benchmark EvalQABench, and an open-source model.\n\n**W2-1: Is LLaVA1.5 stronger than Fuyu-8B?**\n\nIt is not appropriate to claim that LLaVA1.5 [5] is stronger than Fuyu-8B. As Fuyu-8B is trained with large-scale VL data, LLaVA1.5 is only trained with about 1.4M data in the whole two-stage training. The zero-shot results provided by Fuyu-8B (https://www.adept.ai/blog/fuyu-8b) show that even without training with the VQAv2 and GQA datasets (training data of LLaVA1.5), Fuyu-8B achieves exceptional performance on multiple VQA datasets.\n\n**W2-2: Are the generated negative samples easier to recognize or of lower quality?**\n\nFirstly, the generated samples are not simple for model training. As shown in Table 7, these state-of-the-art (SOTA) MLLMs still suffer from inferior accuracy on the generated data, indicating the generated data remains challenging. \n\nSecondly, LLaVA1.5 is prone to predicting “Yes” (nearly 66%), as shown in Table 7, indicating that the EvalQABench samples are not easy for LLaVA1.5 to identify. \n\nThirdly, the EvalQA task differs from the VQA task. If we use Fuyu-8B or GPT-4 to build VQA synthetic datasets and then use the synthetic data to train the model, it may lead to model degradation. However, we only use Fuyu-8B to produce negative answers for constructing negative samples for model assessment. Thus, EvalQA data would not restrict the VQA performance.\n\n**W3: In-depth analysis of potential data biases.**\n\n(1) For GenQA, we build on existing annotated datasets from the original LLaVA1.5 training data. Since we incorporate not only generic VQA data but also Multi-Choice VQA, Multi-turn VQA, and grounding like REC and REG, our GenQA dataset is diverse. As shown in Table 1, we strictly follow the data ratio of LLaVA1.5 for each data type. Thus, our experimental settings have no obvious data biases in terms of data types and amounts.\n\n(2) For EvalQA, we use the VQAv2 dataset, a subset of the original training corpus of LLaVA1.5. Therefore, no new datasets are used. Moreover, VQAv2 is already a balanced version of VQAv1. We create the EvalQABench train set by randomly selecting 100,000 samples from the VQAv2 train set and finally yielding 32,000 negative samples (see Appendix D). As shown in Table 2, we have 9 question types of EvalQA tasks, for each type, with manually restricted ratios for data balance.\n\n**W4: Comparison with other datasets or benchmarks.**\n\nThanks for your suggestion regarding the comparison with other datasets. Here are the unique aspects of GenQA and EvalQA:\n\n(1) Unlike the traditional Visual Question Generation (VQG) task, which focuses primarily on the generic VQA domain, GenQA is designed to generate diverse VQA data. Additionally, GenQA generates both questions and answers simultaneously, whereas traditional VQG focuses solely on question generation.\n\n(2) EvalQABench is designed to assess VQA data rather than answer VQA questions. In contrast, other VQA benchmarks (e.g., VQA-GEN [6], CrossVQA [7], OVQA [8], STAR [9]) and multimodal benchmarks (e.g., LLaVA-Bench, SEED-Bench, SOK-Bench [10], CinePile [11]) primarily evaluate a model's ability to answer questions.\n\nCompared to the datasets you mentioned:\n\n(1) The data generation process of CrossVQA contains the joint question and answer generation. However, our GenQA is considered an additional task for enhancing the model's comprehension ability, not a test dataset for assessing distribution shifts.\n\n(2) OVQA and STAR are video VQA datasets that focus on question answering.\n\n(3) SOK-Bench (May 15, 2024) and CinePile (May 14, 2024) are contemporary works that use ChatGPT for data generation. In contrast, our EvalQABench uses only non-commercial models.\n\nThank you for recommending these datasets. We will add the comparison in the next version of Section 2.\n\n**W5: The paper does not provide the generated dataset for review.**\n\nWe create an anonymous link at https://anonymous.4open.science/r/LOVA3-9008/README.md due to the double-blind policy containing all our created datasets of EvalQABench. Please refer it for further details.\n\n**Q1: The prompt stability of the QA generation**\n\nWe train the GenQA task by randomly choosing one from 58 prompts for each data sample along with a short description for the data type, such as “Please provide a clear and direct question and answer after examining the image. This is a Multi-choice VQA task.” Thus, it is stable for QA generation when one prompt is randomly chosen during inference.\n\n**Q2: Why apply Fuyu-8B for the data generation?**\n\nWe chose Fuyu-8B because it is a fast, open-source MLLM with exceptional performance on many complex tasks. Through our preliminary study, we found that Fuyu-8B is stronger than LLaVA1.5 with fewer hallucination issues and better visual input understanding ability.", "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": "4jY6XOgKog", "reviewer": "Reviewer_4XZr", "summary": "This paper enhances the MLLM's visual understanding capability by training it to ask questions about an image and evaluate the correctness of given question-answer pairs about an image. To achieve this goal, new data is extracted from existing datasets and a new model is fine-tuned on the new data. The experiment shows that the newly added data can improve the MLLM's capability of understanding of images with higher scores on VQA tasks.", "questions": "(Table 6) Why does adding GenQA-Grounding data improves ScienceQA performance?", "limitations": "The paper mentions some limitations of the proposed pipeline. However, as mentioned above, the biggest limitation is the limited scope of the considered setting which only involves VQA (including grounding) problems without considering the embodiment of MLLMs.", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is generally well-written and easy to understand.\n- The argument that training a MLLM to ask questions and evaluate answers can improve its visual understanding is reasonable and, verified by the well-conducted experiments in the paper.\n- The experiment setups are carefully designed to avoid unfair comparisons.", "weaknesses": "- The three key capabilities of MLLMs covered by the paper--asking, answering, and evaluation--should be characterized in an interactive environment (like in an embodied environment where the MLLM is treated as the high-level perceiver/planner/controller of robots) instead of in the static environment. Consider, for example, an MLLM doing an embodied task that needs asking about some key questions, this is where the asking capabilities really make sense. However, the paper only trains and evaluates the MLLM in simple VQA problems as in previous literature. In the paper's current state, the value of the paper is limited and, from my perspective, does not meet the bar of acceptance if VQA tasks are considered only. The scope of the paper needs to be increased to a significant extent that touches the essence of MLLMs with higher-level capabilities that incorporate iterative/interactive thinking and planning.\n\n- The added synthesized data only gives the model a limited improvement in performance, while adding a large amount of computation overhead. In fact, if we use models like GPT-4(V) to synthesize random VQA data,  the performance will increase as well [1], so I do not see the clear benefit of specifically doing the asking and evaluation data augmentation. This problem is relevant to the first problem: the capability added to MLLM should not be evaluated in VQA tasks.\n\n[1] Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and valuable review. We thank your suggestions about extending the research scope to other domains.\n\n**W1-1: Why do we apply three key capabilities in the current static environment rather than in an interactive environment?**\n\nFirstly, it should be noted that these three abilities are not exclusive to interactive environments such as embodied AI. Other research domains, such as GUI Assistant [12], AI Medical Diagnosis [13], and LLM-Agent [14, 15], also involve asking and assessments in their systems. Both static and interactive environments require the incorporation of these three capabilities. Therefore, incorporating these abilities into MLLM model training can be recognized as one strength of our work that other MLLMs neglect.\n\nSecondly, the scope of this research is about training MLLM. We are motivated by the current model training of MLLMs that primarily focuses on teaching models to answer the questions while neglecting the asking and assessing abilities. Our investigation of current MLLMs, including LLaVA, Qwen-VL [16], and CogVLM [17], revealed that while these models excel at answering questions, they perform inadequately in asking and assessing skills. Therefore, we consider that it is necessary to explore adding asking and assessing abilities in training MLLM. Our experiments in Tables 3, 4, and 5 reconfirm that these three abilities actually enhance models’ problem-solving capabilities.\n\n\n**W1-2: Is it appropriate to say that the asking capability of an MLLM makes more sense in embodied tasks?**\n\n(1) We agree that asking key questions is important for embodied tasks. However, other tasks, such as AI diagnosis and AI assistant systems, also require this ability. It is not exclusive to embodied tasks. In other words, this ability is applicable not only to embodied tasks but also to other domains and is worth exploring in MLLM training.\n\n(2) We assume that if an MLLM is able to yield high-quality questions and corresponding answers, it indicates a stronger problem-solving ability and a deep visual understanding. This is similar to human cognition: after thoughtfully understanding concepts, we can well ask questions. Refer to the ablation study in Table 6, by adding GenQA data, there are consistent improvements in VQA datasets. Therefore, we consider adding asking tasks to be essential in MLLM training.\n\n(3) Highlighting the importance of asking ability is also the affirmation of our contribution about incorpararting GenQA in the model training, which other MLLMs have neglected.\n\n**W1-3: Is it acceptable for testing on VQA and multimodal benchmarks?**\n\nFirstly, we follow the previous representative MLLMs, such as LLaVA1.5, InstructBLIP, Qwen-VL, and CogVLM, to test our model on widely used VQA datasets and multimodal benchmarks so that we can directly compare our results with theirs.\n\nSecondly, VQAv2, GQA, VizWiz, ScienceQA, and the object hallucination VQA dataset POPE are widely used VQA datasets for testing MLLMs. Additionally, we select five popular multimodal benchmarks—MME [18], SEED-Bench, MMBench [19], LLaVA-Bench, and MM-Vet [20]—which encompass a wide range of diverse and challenging multimodal questions across various domains (e.g., Math, Instance Attribute, Spatial Relation, Text Understanding, Object Recognition, Scene Understanding) especially for testing MLLMs.\n\nThirdly, we appreciate your suggestion to extend the scope of our research to embodied AI and consider it a potential area for future exploration.\n\n**W2-1: The added synthesized data only gives the model a limited improvement in performance while adding a large amount of computation overhead.**\n\n(1) The improvement is not limited, as we chose LLaVA1.5 (7B) as the baseline and trained the model following the original training recipe without tuning any hyperparameters. Additionally, we did not involve any new images in training. Therefore, the clear improvements shown in Tables 3, 4, and 5 are meaningful. Some results of our 7B model are even compatible with the larger model LLaVA1.5 (13B): \n\n\n| | Size | VQAv2 | GQA | VizWiz | MME |\n| --- | --- | --- | --- | --- | --- |\n| LLaVA1.5 | 7B | 78.5 | 62.0 | 50.0 | 1510.7 |\n| LLaVA1.5 | 13B | 80.0 | 63.3 | 53.6 | 1531.3|\n| LOVA$^3$ | 7B | 80.3 | 63.3 | 53.6 | 1552.7 |\n\n(2) The training costs are not high. We trained our model for only 24.5 hours on an 8 A100 (40G) GPU setup.\n\n(3) As shown in Table 6, the first six rows, which present results with different data sizes, consistently outperform the baseline model LLaVA1.5. This indicates that our training paradigm performs well even with smaller data sizes, including as low as 64K data points in EvalQA.\n\n**W2-2: If we use models like GPT-4(V) to synthesize random VQA data, the performance will increase as well.**\n\nSynthesizing data to improve performance is non-trivial and is crucial for training MLLMs. The key challenge is how to generate essential data at a low cost. We propose a solution that does not require GPT-4(V) or new images. Instead, it leverages existing annotations to achieve a significant performance improvement. For the GenQA task, the training data is derived from the original training datasets used in LLaVA1.5, such as VQAv2 and GQA. For the EvalQA task, we use VQAv2 as the source for data generation. As shown in the ablation study in Table 6, there are robust gains by adding GenQA and EvalQA data. These results indicate the effectiveness of our created data.\n\n**Q1: Why does adding GenQA-Grounding data improve ScienceQA performance?**\n\n(1) Training with GenQA-Grounding data enhances the ability to understand object positions deeply, which is beneficial for fully leveraging visual information for reasoning.\n\n(2) Many images in ScienceQA [21] (https://scienceqa.github.io/)  are from natural scenes. Therefore, enhanced visual understanding would improve the accuracy of reasoning is reasonable.", "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": "PSC4de1UOM", "reviewer": "Reviewer_5aGT", "summary": "This paper augments presents a data augmentation / multi-task learning technique to improve model quality for Visual Question Answering (VQA). The key idea of the paper, motivated by analogy to humans, is that asking questions and assessing answers are also key skills, apart from just answering questions. The paper seeks to train a model to \"Answer, Assess, Ask\" jointly, relying existing datasets for a variety of answering and asking tasks, and deriving a new dataset called EvalQABench for the assessment task. The introduction of the EvalQABench dataset, initially created by LLMs and later filtered by experts, is another potentially valuable and lasting contribution. Multiple tasks on augmented data are implemented on the LLava backbone which is an existing SOTA model. The paper compares their technique (called LOVA) to several SOTA models on a variety of datasets showing robust gains in a multitude of settings, providing confidence in the technique's validity.", "questions": "In section 1, there's a claim that the EvalQABench datasets is generated via a \"new automatic pipeline\". However, in section 3.2 the authors say \"... acknowledging that the Fuyu-8B model is not flawless and recognizing that no multimodal model, including GPT-4V, is perfect, we have implemented both manual filtering and error correction...\". Do the earlier claims about the pipeline being automatic overstate the case? Are they necessary? \n\nDoes the feedback add value beyond just rephrasing the answer is a longer sentence. A lot of the feedback seems trivial and already captured in the q,a pair. For e.g. \"What does the woman have on her back\". \"backpack\" vs \"No, the woman has a backpack on her back\".  As another e.g. \"What are the people doing?\", \"motorcycling\", vs \"No, the people in the picture are motorcycling\".", "limitations": "Smaller models sizes are an understandable limitation as mentioned in the paper, and referenced earlier in the review.\n\nThe introduction of EvalQA dataset and the additional \"asses\" task is the key incremental contribution of the paper. By looking at rows 4 and 7 in Table 6 that shows ablation studies, one could discern the incremental benefit of EvalQA. The deltas in the scores are somewhat underwhelming (unclear if they are significant).", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well motivated: assessing and asking (evaluation and question generation) are closely associated tasks to question answering, that can be performed on datasets that are easily derived from question answering datasets. The argument that training on these closely related tasks improves generalization on question answering is intuitive though reliant on analogy with humans, which has its own traps. \n\nThe paper evaluates their technique against a variety of SOTA models, and across a multitude of tasks, proving that the gains are robust. The paper also provides ablation studies for various components, showing their utility. In general the experiment section is detailed, extensive and is a highlight of the paper. \n\nThe paper has 100 citations, and extensive references to related work, making it easier to assess the novelty of the work.", "weaknesses": "As the authors point out, due to cost considerations, the authors only evaluate the technique on smaller (relative to MLLMs) models. This is important as model size is a confounder when it comes to assessing the usefulness of data augmentation or multi-tasks. A technique useful for a 7B model is not necessarily useful for a 70B model. However, given the cost of inference of larger models, improving smaller models to be competitive with larger models has its own benefit. \n\nThere is prior work that already includes question answering and question generation, for example the InstructBLIP paper. Viewed in that sense, this paper makes an incremental contribution adding the assessing task to the answering, asking combination that was already shown to be useful earlier. However, the EvalQABench dataset is potentially very useful for the whole subfield of visual question answering. One minor but interesting finding in the paper is that in a balanced dataset split rightdown with model with 50% Yes and 50% No answers, not all models predict Yes/No close to 50% of the time.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thorough review! We sincerely appreciate your acknowledgment of LOVA3’s motivation, clarity, novelty, effectiveness, and consistent performance gains.\n\n**W1: The model size.**\n\nWe sincerely thank your valuable comments. Due to limited GPU resources, it is hard for us to train with the larger model over 7B. Moreover, we found that the results of our model are compatible with LLaVA1.5 (13B) on VQAv2 (80.3 > 80.0), GQA (63.3 = 63.3), MME (1552.7 > 1531.3). This result demonstrates the effectiveness of our training paradigm.\n\n**W2: Training Data used in InstructBLIP.**\n\nWe agree that there are question generation datasets in InstructBLIP, but our GenQA has the following differences:\n\n(1) We ask the model to generate questions and answers jointly, not only the question.\n\n(2) We use not only generic data types but also multi-choice VQA, multi-turn VQA, and two grounding data types REC and REG for GenQA task. This increases the difficulty of the GenQA task and enhances the model's problem-solving abilities.\n\n**Q1: Claim of  \"New automatic pipeline.\"**\n\nThank you for your suggestion. The term \"New automatic pipeline\" in Section 1 refers to the automatic data generation process and does not include the data filtering process, which could lead to confusion. We will revise this in the next version.\n\n**Q2: \"Does the feedback add value beyond just rephrasing the answer in a longer sentence? A lot of the feedback seems trivial …\"**\n\nYes, the feedback is generated by rephrasing the question and answer. \n\nFirstly, we want the model not only to give the classification of \"Yes/No\", but we would also like there exists a simple explanation. Thus, we use the LLaMA-2 to rephrase the question and the ground-truth answer to generate feedback. During training, the ground-truth answer and negative answer do not appear simultaneously. It is not trivial to add such feedback. \n\nSecondly, rephrasing the ground-truth answer and question is an efficient way to create data. Generating feedback for 32,000 images manually is impractical, so we utilize LLM assistance. Our preliminary study shows that this strategy ensures high accuracy in feedback generation and is effective for training the model to assess the VQA triplet.\n\n**Limitation: About the results of EvalQA in Table 6**\n\nThe reason for the incremental results in Table 6 of EvalQA data may be the following:\n\n(1) Data Size: Compared to the other two tasks, VQA (665K) and GenQA (842K), EvalQA includes only 64K data points. \n\n(2) Data ratio: It is crucial for training MLLMs, as highlighted by InstructBLIP and LLaVA1.5. Therefore, the smaller data size of the EvalQA task affects the results in rows 4 and 7 of Table 6. However, referring to row 3 in Table 6, one can observe improvements even with only 64K additional data included.", "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:

Meta-review: The paper received divergent ratings of reject (R1), borderline reject (R2) and accept (R3) post rebuttal and post the discussion between the authors and reviewers and the discussion amongst the reviewers. After carefully reading the paper, the reviews, the author rebuttal and all the discussion, the AC recommends accepting the paper because they find the proposed idea of using two additional training tasks of asking and assessing question-answer pairs on top of the standard VQA training task to be novel and interesting -- the paper shows that the generalization abilities of multimodal LLMs such as LLaVA improves significantly (significant performance improvements shown across 10 benchmarks!) when it is trained with two auxiliary tasks on top of the standard visual question answering task -- 1) generating question-answer pairs for given images, 2) judging the correctness of answer given questions and images. Moreover the training datasets for these additional tasks are obtained from datasets that are already used to train the base LLaVA model, thus validating that the performance improvements brought about the proposed method are not merely due to using additional data. The proposed methods leads to significant performance improvements across 10 benchmarks validating the efficacy of the proposed method.

Thus, the paper presents a new training recipe for training multimodal LLMs which does not require any additional images and requires minimal additional annotations (only to obtain negative answers for the EvalQA task) and leads to significant performance improvements across various benchmarks. Thus the proposed method is easy to adopt and can significantly benefit the community by providing a new training recipe for building stronger multimodal LLMs.

The AC is not convinced by R1's and R2's reasons for rejection. Below are the AC's thoughts for each of R1's and R2's outstanding concerns post-rebuttal and post-discussion:

**R1**

> "The response claims that the contributions include proposing two novel tasks (GenQA and EvalQA), but it lacks a clear explanation of how these tasks differ from existing data generation or evaluation tasks used in QA or vision-language contexts. Without concrete distinctions, the novelty of these contributions is unclear."

Authors state that their novelty lies in proposing these tasks as additional tasks for training while using the existing datasets. The AC is convinced with this response from the authors.

> "Why does the work apply Fuyu-8B instead of LLaVA 1.5 for the data generation"

The AC is not worried about this because despite using Fuyu for data generation, which is supposedly weaker than LLaVA as per R1, the proposed method brings about performance improvement in LLaVA.

> "While Table 1 lists existing relevant datasets and their respective sizes, it does not justify why the sum of the ratios presented should be considered an ideal data distribution."

Authors say they follow the data distribution used for training LLaVA.

> "Additionally, no data distributions are provided in Table 2 (which contains only a few examples) or in Appendix D (which details the data amounts at various processing stages). This omission makes it difficult to evaluate the appropriateness of the proposed data distribution -- what data distribution."

These are examples from EvalQA for which authors state they use VQA v2.


**R2**

> "but if your method is general and most likely to be useful for some specific task (here interactive agents), the evaluation should better reflect this point."

The AC is convinced with the author response on this point. The AC agrees that evaluating on VQA tasks only is a useful and sufficient evaluation given the scope of the paper. The evaluation suggested by R2 in embodied environments appears to be out of scope for the paper.

> "The added synthesized data only gives the model a limited improvement in performance, while adding a large amount of computation overhead."

Although the authors provided the training time for the proposed method, for precise comparison, the AC recommends the authors to also provide the training time for the LLaVA baseline.

> "If we use models like GPT-4(V) to synthesize random VQA data, the performance will increase as well [1], so I do not see the clear benefit of specifically doing the asking and evaluation data augmentation."

The AC is convinced with the author response on this that the benefit of the approach is that it makes use of existing datasets without needed additional annotations. Although the EvalAI task does require additional annotations (for negative answers), as shown in Table 6, the proposed method improves over the baseline even in the absence of this task. So it is clear that the performance improvement is not solely due to additional annotations. 

Overall, the AC thinks the proposed method would be of value to the community and hence recommends accepting the paper. The AC recommends the authors to incorporate the additional clarifications and experimental results provided in the rebuttal in the camera-ready version of the paper.

The AC is also convinced with the author response to the Ethics reviews but recommends the authors to provide the precise error patters that were automatically detected and filtered out in the filtering stage of curating the EvalQA training dataset.
Decision: Accept (poster)