Title: LOVA3: Learning to Visual Question Answering, Asking and Assessment
Venue: NeurIPS 2024
Accepted: yes
Keywords: Multimodal Large Language Model

Instruction Tuning

Visual Question Answering

Visual Question Assessment.
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

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. Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills. Inspired by the human learning mechanism, we introduce LOVA 3 , 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 validation and 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 LOVA 3 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. The code is available at https://github.com/showlab/LOVA3.

1 Introduction

To acquire knowledge, we humans often answer lots of questions and then improve ourselves by comparing our answers with the ground-truth answers. As a 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 in higher value than solving it.' Georg Cantor [6]

asking a question is very valuable and even more important than answering a 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 a deep understanding of our diverse world [86, 67].

VQA Ability

EvalQA Ability

Prompt

:

Is this a filled donut? Answer

the question using a single word or

phrase.

: Yes

LOVA

3

(Ours)

: Yes

Ground Truth

: Yes

Prompt

:

Please examine the correctness of

this ques;on and answer according to the

image content. Ques;on: Is this a filled

donut?\nAnswer: no

: Yes

LOVA

3

(Ours)

: No, this is a filled donut.

Prompt

:

Analyze the image and respond with a

direct question and answer.

: What is the color of the pink frosted

donut?\nAnswer: Pink

LOVA

3

(Ours)

:

Question: What is the donut on the left side of

the image called?

A. apple fritter B. long john C. eclair D. cruller

Answer: D

GenQA Ability

Image

Although innate to humans, apart from answering ability, two other learning mechanisms of asking and assessment remain underexplored for contemporary multimodal large language models (MLLMs). Current MLLMs [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 predominantly revolve around visual question answering (VQA). As a result, as shown in Fig. 1, current MLLMs, e.g., the representative LLaVA-1.5 [42], suffer from inferior performance on asking questions and self-assess question-answer pairs (QA), which underscores their efficacy as problem-solvers and prohibits holistic multimodal understanding.

To advance the comprehensive intelligence of MLLMs, we introduce two essential tasks: GenQA and EvalQA , aiming at bolstering the intelligence and robustness of MLLMs. GenQA focuses on enabling the model to generate diverse question-answer (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-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 MLLM to extract fine-grained visual cues from images, such as explicit object localization and compositional relationships. This, in turn, enhances the multimodal reasoning ability of MLLMs. During training, we gather the relevant datasets for these tasks and transform them into a generative format using our proposed instruction template. EvalQA, on the other hand, involves tasking the MLLM to predict the correctness of a given visual-question-answer triplet. Recognizing the absence of datasets specifically designed to assess VQA correctness, we have developed a new benchmark called EvalQABench for evaluating VQA data. Rather than asking humans to label such a dataset, we propose a 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 a one-sentence explanation as the feedback. For instance, 'Yes, the oranges are not in a bag' .

By integrating the GenQA and EvalQA tasks into the vanilla multimodal learning, we develop an effective training framework called LOVA 3 . In this study, we select the SOTA MLLM LLaVA-1.5 as the backbone model for evaluation. We conduct experiments on 10 widely used multimodal benchmarks such as GQA [30], VQAv2 [26], Vizwiz [27], MME [19], MMBench [45], and MMvet [92], and observe consistent improvements across these benchmarks. To summarize, our proposed LOVA 3 is a 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:

To the best of our knowledge, LOVA 3 is the first effort to imbue the asking and assessment abilities in training a robust and intelligent MLLM. LOVA 3 open an avenue for imitating the human abilities towards holistic intelligence for MLLM.

We build a new benchmark EvalQABench for the VQA evaluation as the first effort to advance the VQA data assessment of future research.

The experimental results demonstrate that training with LOVA 3 consistently improves performance across several multimodal benchmarks, including VQAv2, GQA, MME, VizWiz, MMBench, and MM-Vet, etc.

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 a wide range of complex tasks to play an important role in assisting humans in daily life. Equipped with these LLMs, 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 a 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 to construct their instruction tuning dataset. It adopts the VQG task but is limited in generic data type. Different from InstructBLIP, we propose the GenQA task for jointly generated questions and answers on 5 primary VL 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 a large-scale image-text corpus for instruction tuning, Qwen-VL [3], CogVLM [84], AnyMAL [58] and Chameleon [79] achieve exceptional performance on 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 a 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], knowledge-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 a complementary task can enhance the robustness of visual question answering. This finding reaffirms our motivation that training a model to generate diverse questions contributes to a 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, 71]. 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-based pipeline. This pipeline can automatically generate feedback, paving the way for enhanced automated data processing in the future.

3 Methodology

In this section, we introduce LOVA 3 , a 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 to 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, e.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, we include two multimodal grounding tasks: Referring Expression Comprehension (REC) and Referring Expression Generation (REG). Generating REC and REG data requires a deeper understanding of image content, enabling the model to 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-grained 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 constructing negative answers. When dealing with large-scale ground-truth VQA pairs, how can we automatically produce the negative answer? One viable solution is 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:

Question Types

Object

Yes/No

Counting

Image

Question

What kind of flowers are on

the picture to the left?

Is the sun shining?

How many vases are there?

Ground-truth Answer

roses

no

6

Negative Answer

pansy

yes

5

Feedback

No, the left of the picture

shows roses.

No, the sun is not shining.

No, there are 6 vases in the

picture.

Question Types

Color

Attribute

Number

Image

Question

What color is the truck?

What type of tree is on the

right?

What number is written on the

sheep?

Ground-truth Answer

silver

cherry

3

Negative Answer

white

palm

5

Feedback

No, the truck is silver.

No, the tree on the right is a

cherry tree.

The number written on the

sheep is 3.

Question Types

Relation

Action

Other

Image

Question

What does the woman have on

her back?

What are the people doing?

What does the second sign

say?

Ground-truth Answer

backpack

motorcycling

all-war

Negative Answer

jacket

riding bikes

stop

Feedback

No, the woman has a backpack

on her back.

No, the people in the picture

are motorcycling.

No, the second sign says

'all-war'

ALL-WAR

<Img> This is the question: <Q> . Please give me the wrong answer to this question. The answer should be a single word or phrase. \ n

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. 2.

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 it with a random number. After applying the above filtering and correction processes, we found that most of the incorrect samples had been removed.

VQA

MLLM

(Fuyu-8B)

Question

: What kind of

flowers are on the

picture to the left?

Answer

: roses

Negative Answer

Question

: What kind of

flowers are on the

picture to the left?

Positive answer

: roses

Negative answer

: pansy

LLM

(Llama 2)

EvalQA with feedback

Question

: What kind of flowers

are on the picture to the left?

Positive answer

: roses

Negative answer

: pansy

Feedback

: No, the left of the

picture shows roses.

Manual

Filtering

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, we can get feedback like 'No, the left of the picture shows roses. ' . Moreover, we use similar manual filtering strategies that are used in the negative answer generation step to remove the noisy samples with wrong formats or 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 a final set of 41,592 samples. For the training set of EvalQABench, we create a one-positive-one-negative format by randomly selecting 32,000 negative samples from the 41,592 filtered samples, yielding a 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, we 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 3 . This model is built upon the prevalent MLLM LLaVA-1.5 [42] with three key components: Vision Encoder, MLP Adapter, and Large Language Model. For the vision encoder, we follow LLaVA-1.5 [42] and implement it with a 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-7B [15]. Following [42], the MLP adapter is a simple two-layer MLP since such a simple design is better for reserving the visual information while achieving running efficiency. In this study, we leverage LLaV A-1.5 to build upon because of its exceptional performance and highly reproducible training and validating codes. Other outstanding MLLMs, such as CogVLM [84] and Qwen-VL [3], are pre-trained on billions-scale datasets or in-house datasets. This scale of data makes the training process difficult to replicate and poses challenges in incorporating our proposed training tasks, GenQA and EvalQA.

3.4 Training

For brevity, we denote the LOVA 3 model as F M . Given an image X I , our target is to enforce F M to generate the response X R :

"question": "What is the cat lying

on?',

"answer": "table',

"fuyu_answer": "cat"

Answer appear in the question

"question": "Who is in the

photo?", "answer": "man",

"fuyu_answer": "man"

Repeat the ground truth answer

"question": "Is this a little boy's or little

girl's room?",

"answer": "boy",

"fuyu_answer": "no"

Different answer type

"question": "How many sinks?", "answer": "2", "fuyu_answer": "two"

Numberrepresentation issue

Manual Filtering

Manual Correction

"question": "Are these buildings less

than 10 years old?",

"answer": "no",

"fuyu_answer": "new" -

> '

yes"

Convert the text to yes/no

"question": "What number is on the

girls shirt?",

"answer": "12",

"fuyu_answer": "two " -

> '

2"

Convert text to number

We follow previous MLLMs[42, 17], and design the training objective in an autoregressive manner:

where x i is the current prediction token and L denotes the response sequence length. θ denotes the trainable parameters.

4 Experiments

4.1 Datasets and Settings

Training Datasets. For the fair comparison, we utilize the 665K instruction-following dataset introduced in LLaVA1.5, combined with the 842K GenQA data as outlined in Tab. 1, and an additional 64K data comprising one-positive-one-negative pairs as described in Section 3.2, totaling our training datasets with 1.5M samples. It is important to note that the datasets and annotations used in both VQA and GenQA are the same. There are no additional datasets involved, thus avoiding unfair comparisons caused by the introduction of new instruction data. For EvalQA, we adopt VQAv2 to build the training set, which is already included in the original 665K instruction dataset.

Validation Datasets. We assess LOVA 3 on 10 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] is a 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] is a benchmark comprising 21k multimodal multiple-choice questions with diverse science topics. (4) POPE [39] is a 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 to evaluate various capabilities of MLLMs, including object existence, color recognition, counting, OCR, etc.

Competitors. We compare LOVA 3 with other SOTA models inlcuding MiniGPT-4 [101], BLIP2 [37], InstructBLIP [17], mPLUG-owl [89], LLaMA-AdapterV2 [22] and LLaVA-1.5 [42]. We report the results from their paper or the benchmark leaderboard.

Implementation Details. To ensure a fair comparison, we train the LOV A 3 -7B model without tuning any hyperparameters of LLaVA-1.5 [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 AdamW [46] optimizer with a learning rate of 2 × 10 -5 and a 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[15] to Phi-1.5B[41] to evaluate smaller LLMs. We train LLaVA-Phi-1.5 and LOVA 3 -1.5B by using the same training recipe. The only difference of training with Phi-1.5 is that we increase the learning rate from 2 × 10 -5 to 4 × 10 -5 to ensure the higher performance. The model LLaVA-Phi-1.5 is trained with the original 665K VQA instruction data as the baseline. The model LOVA 3 -1.5B is trained with our proposed 1.5M mixture data including VQA, GenQA, EvalQA data.

4.2 Main Results

Generic tasks. As shown in Tab. 3, LOVA 3 -7B outperforms LLaVA1.5 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 LLMs, our LOVA 3 -1.5B outperforms the baseline LLaVA-Phi-1.5 on VQAv2, GQA, VizWiz, and ScienceQA by 2.6%, 2.5%, 3.4%, and 0.5%, respectively. This demonstrates a consistent improvement when training with our LOVA 3 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 LOVA 3 framework for this VQA task.

MME, SEED-Bench, MMBench, LLaVA-Bench. In Tab. 4, we evaluate four prevalent multimodal benchmarks, where our LOVA 3 -7B surpasses LLaVA1.5 with 42.0% on MME benchmark, 0.9% increase in accuracy on SEED-Bench, 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 LLaVA1.5, which is solely trained with VQA tasks. By investigating the results produced by LOVA 3 -1.5B on these multimodal benchmarks, one can see greater improvements in the smaller models. Notably, LOVA 3 -1.5B achieves an impressive 98.2% improvement on the MMEbenchmark. The lower results of LLaVA-Phi-1.5 and LOVA 3 -1.5B on MMBench (Cn) may be attributed to a lack of Chinese training data in the Phi-1.5 training process.

MM-Vet. In Tab. 5, we compare LOVA 3 -7B with other approaches on MM-Vet, which is a challenging 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 LOV A 3 -7B outperforms LLaVA-1.5 by 4.0% at an average. Such improvement demonstrates the effectiveness of LOVA 3 -7B in solving these challenging multimodal questions. Based on the results on LOV A 3 -1.5B and LLaVA-Phi-1.5, one can see a greater improvement than LOVA 3 -7B.

4.3 Ablation Study

We split the data used in the GenQA task into two groups: GenQA-General and GenQA-Grounding. The findings, presented in Tab. 6, are instrumental in investigating the contributions of GenQA and EvalQA to model efficacy. (1) Comparing the first four rows, one can find that both GenQA-General and EvalQA data are more effective in improving performance than GenQA-Grounding. (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-General from the finetuning corpus, the performance drops significantly on MME and VizWiz. (4) Compare the rows 0 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 EvalQA, 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 in Tab. 6.

4.4 Training with Gemini-Generated 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-Flash 1 as 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 or the commercial model Gemini-1.5-Flash, our proposed training paradigm LOVA 3 consistently improves performance across both smaller and larger baseline models.

4.5 Benchmark of EvalQABench

We report the evaluation results on our EvalQABench test set in Tab. 8 to validate the EvalQA ability of current SOTA models and LOVA 3 . We select BLIP2 [37], InstructBLIP [17], CogVLM [84], Qwen-VL-Chat [3], InternLM-XC [95], and LLaVA1.5 [42] for the comparison. We ask these models to answer 'Yes' 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 as "No," which ideally should approximate 50% due to the one-positive-one-negative setting utilized in our 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 3 shows several improvements over our baseline LLaVA1.5 by margins of 14.66%, 17.87%, and 9.92% in Accuracy, Precision, and F1 Score, respectively.

5 Conclusion and Limitations

In this work, we propose a novel multimodal framework, LOVA 3 , which is capable of mimicking the human visual question answering, asking, and assessment to achieve deeper multimodal understanding. We introduce two additional training tasks, GenQA and EvalQA , to help MLLM acquire these abilities. We establish EvalQABench , a novel benchmark to assess the VQA samples between multiple MLLMs. Experimental results show that LOVA 3 achieves superior performance across various benchmarks, including MM-Vet, 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 LOVA 3 could be beneficial for larger LLMs, as other MLLMs have shown performance improvements with increased LLM scale. (2) GenQA and EvalQA as two additional tasks increase training costs, but it is inevitable for an MLLM to acquire new capabilities. (3) Due to the limited scope of instruction tuning datasets, LOV A 3 cannot address domain-specific multimodal tasks well, such as text-centric VQA or mathematic-relevant VQA.

References

Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson. nocaps: novel object captioning at scale. In ICCV , 2019. 3

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

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:2308.12966 , 2023. 2, 3, 6, 8, 10

Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sa˘ gnak Ta¸ sırlar. Introducing our multimodal models, 2023. 2, 4, 9

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. NeurIPS , 2020. 3

Georg Cantor. Über unendliche, lineare Punktmannigfaltigkeiten: Arbeiten zur Mengenlehre aus den Jahren 1872-1884 , volume 2. Springer-Verlag, 2013. 1

Junbum Cha, Wooyoung Kang, Jonghwan Mun, and Byungseok Roh. Honeybee: Localityenhanced projector for multimodal llm. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024. 3

Chi Chen, Ruoyu Qin, Fuwen Luo, Xiaoyue Mi, Peng Li, Maosong Sun, and Yang Liu. Position-enhanced visual instruction tuning for multimodal large language models. arXiv preprint arXiv:2308.13437 , 2023. 3

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 as a unified interface for vision-language multi-task learning. arXiv preprint arXiv:2310.09478 , 2023. 3

Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv:2306.15195 , 2023. 3, 18

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

Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv:1504.00325 , 2015. 3

Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, and MingWei Chang. Can pre-trained vision and language models answer visual information-seeking questions? arXiv preprint arXiv:2302.11713 , 2023. 3

Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, Ji 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. 3

Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023. 3, 6, 7

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 , 2022. 3

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

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 Linguistics , pages 1763-1774, 2018. 3

Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv:2306.13394 , 2023. 2, 3, 7, 8

Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna. Blink: Multimodal large language models can see but not perceive. arXiv preprint arXiv:2404.12390 , 2024. 3

Richard Gale. Asking questions that matter. . . asking questions of value. International Journal for the Scholarship of teaching and learning , 3(2):3, 2009. 1

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, 8

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 a family of multi-modal large language models, 2024. 3

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

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

Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the v 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

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, 3, 7, 8

Muyang He, Yexin Liu, Boya Wu, Jianhao Yuan, Yueze Wang, Tiejun Huang, and Bo Zhao. Efficient multimodal learning from data-centric perspective. arXiv preprint arXiv:2402.11530 , 2024. 3

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:2302.14045 , 2023. 3

Drew A Hudson and Christopher D Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In CVPR , 2019. 1, 2, 3, 4, 7, 8, 17, 18

Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In EMNLP , 2014. 4, 18

Ranjay Krishna, Michael Bernstein, and Li Fei-Fei. Information maximizing visual question generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2008-2018, 2019. 3

Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV , 2017. 4, 18

Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, and Jiaya Jia. Lisa: Reasoning segmentation via large language model. 2024. 3

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 Thirtyseventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2023. 8

Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, and Ying Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension, 2023. 3, 7, 8, 18

Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv:2301.12597 , 2023. 3, 7, 8, 9, 10

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

Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv:2305.10355 , 2023. 3, 7, 8

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 of the IEEE conference on computer vision and pattern recognition , pages 6116-6124, 2018. 2, 3, 4

Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. Textbooks are all you need ii: phi-1.5 technical report. arXiv preprint arXiv:2309.05463 , 2023. 7

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

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS , 2023. 1, 3, 4, 7, 8, 18

Mengchen Liu, Chongyan Chen, and Danna Gurari. An evaluation of gpt-4v and gemini in online vqa, 2024. 3

Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281 , 2023. 2, 3, 7, 8, 18

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations , 2019. 7

Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, et al. Deepseek-vl: towards real-world vision-language understanding. arXiv preprint arXiv:2403.05525 , 2024. 3

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. 3

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

Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-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

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

Arjun Mani, Nobline Yoo, Will Hinthorn, and Olga Russakovsky. Point and ask: Incorporating pointing into visual question answering. arXiv preprint arXiv:2011.13681 , 2020. 17

Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and comprehension of unambiguous object descriptions. In CVPR , 2016. 18

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

Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In WACV , 2021. 3

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

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 11-20, 2018. 1

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 arXiv:2309.16058 , 2023. 3

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

Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende. Generating natural questions about an image. arXiv preprint arXiv:1603.06059 , 2016. 3

OpenAI. Gpt-4 technical report, 2023. 3

Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Grounding multimodal large language models to the world. arXiv:2306.14824 , 2023. 3

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 image-to-sentence models. International Journal of Computer Vision , 123:74-93, 2015. 3

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

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog , 2019. 3

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

Jie Ren, Yao Zhao, Tu Vu, Peter J Liu, and Balaji Lakshminarayanan. Self-evaluation improves selective generation in large language models. arXiv preprint arXiv:2312.09300 , 2023. 1

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. 3

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. 1

Claude Sammut and Ranan B Banerji. Learning concepts by asking questions. Machine learning: An artificial intelligence approach , 2:167-192, 1986. 1

Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. A-okvqa: A benchmark for visual question answering using world knowledge. arXiv , 2022. 3, 4, 18

Thomas Scialom, Patrick Bordes, Paul-Alexis Dray, Jacopo Staiano, and Patrick Gallinari. What bert sees: Cross-modal transfer for visual question generation. arXiv preprint arXiv:2002.10832 , 2020. 3

Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh. Cycle-consistency for robust visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6649-6658, 2019. 2, 3, 4

ShareGPT. https://sharegpt.com/ , 2023. 18

Oleksii Sidorov, Ronghang Hu, Marcus Rohrbach, and Amanpreet Singh. Textcaps: a dataset for image captioning with reading comprehension. In ECCV , 2020. 3, 18

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 Pattern Recognition (CVPR) , June 2019. 3, 20

Dingjie Song, Shunian Chen, Guiming Hardy Chen, Fei Yu, Xiang Wan, and Benyou Wang. Milebench: Benchmarking mllms in long context, 2024. 3

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

Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models, 2024. 3

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

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv:2302.13971 , 2023. 3

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 , 2023. 3, 6

Nihir Vedd, Zixu Wang, Marek Rei, Yishu Miao, and Lucia Specia. Guiding visual question generation. arXiv preprint arXiv:2110.08226 , 2021. 3

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, 6, 10

Wenhai Wang, Zhe Chen, Xiaokang Chen, Jiannan Wu, Xizhou Zhu, Gang Zeng, Ping Luo, Tong Lu, Jie Zhou, Yu Qiao, et al. Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. arXiv preprint arXiv:2305.11175 , 2023. 3

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 Information Processing Systems , 36, 2024. 1

Xing Xu, Tan Wang, Yang Yang, Alan Hanjalic, and Heng Tao Shen. Radial graph convolutional network for visual question generation. IEEE transactions on neural networks and learning systems , 32(4):1654-1667, 2020. 3

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. arXiv:2304.14178 , 2023. 3

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

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

Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. ACL , 2014. 3

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 capabilities. arXiv preprint arXiv:2308.02490 , 2023. 2, 3, 7, 9, 20

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

Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6720-6731, 2019. 3

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. Internlm-xcomposer: A vision-language large model for advanced text-image comprehension and composition. arXiv preprint arXiv:2309.15112 , 2023. 2, 10

Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Peng Gao, and Hongsheng Li. Mathverse: Does your multimodal llm truly see the diagrams in visual math problems? arXiv preprint arXiv:2403.14624 , 2024. 3

Shijie Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, and Jiawan Zhang. Automatic generation of grounded visual questions. arXiv preprint arXiv:1612.06530 , 2016. 3

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:2205.01068 , 2022. 3

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

Henry Hengyuan Zhao, Pan Zhou, and Mike Zheng Shou. Genixer: Empowering multimodal large language models as a powerful data generator. arXiv preprint arXiv:2312.06731 , 2023. 3

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv:2304.10592 , 2023. 3, 7, 9

A Broader Impacts

In this paper, we propose a 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 to pay more attention to visual question asking and assessment. For these two tasks, there still exists some space for involving more GenQA tasks and more formulations of EvalQA.

B Additional Details of Implementation

665K instruction tuning data. We follow the backbone MLLM LLaVA1.5 to adopt the 665K instruction-following 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.

Model training. The baseline model LLaVA1.5 [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 finetuning the two-layer MLP adapter. In this paper, we are investigating the effectiveness 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 as 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 X I to align the learned visual features with the text input X T . Next, the Multi-Layer Perceptron (MLP) adapter projects the visual feature F I to Z I . Finally, the large language model utilizes Z I and the current text embedding Z T to predict the response in a left-to-right manner. During training, the data of the three tasks is mixed. By such joint training, MLLM exhibits deeper comprehension and promising reasoning ability.

Vision

Encoder

𝑿

𝑰

…

…

𝑭

𝑰

𝒁

𝑻

𝒁

𝑰

…

❄

🔥

🔥

What color is the pot?

blue

Can you provide a concise

question and answer based on

the image?

GenQA

EvalQA

What kind of flowers are on the picture to the

left?\nAnswer: pansy. \nPlease examine the

correctness of this question and answer

according to the image content. Output Yes

or No with the feedback.

Question: What color is

the pot?\nAnswer: blue.

No, the left of the picture

shows roses.

VQA

Hyperparameter. The hyperparameters of LOVA 3 are aligned with those of LLaVA1.5 to 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 LOVA 3 .

C Details of GenQA Data

Generic VQA. It includes four datasets: VQAv2 [26], GQA [30], OCR-VQA [56] and Counting110K [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 LOVA 3 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.Counting110K † , a dataset developed in-house by reformulating the original PointQA [52]

format, contributes to the counting type data generation. Moreover, for most of the above generic VQA with a short response, it is necessary for MLLM to learn to ask questions with long answers. Thus, we leverage the conversation subset of LLaVA-150K [43] and then filter out overly lengthy sentences (e.g., the word number ≥ 200.) to yield LLaVA-250K † 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 a better way to 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 a higher level of complexity for model learning. Thus, We proceed to make the LOVA 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 LOVA 3 , enabling it to master the art of generating varied questions within a dialogue context from a 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 a region precisely when asking or answering a question demonstrates a strong capability of multimodal reasoning. Possessing this capability would enhance an MLLM's potential as an intelligent assistant in human-AI 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 an 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 LOVA 3 ? We provide some examples of prompting LOVA 3 to generate VQA pairs as in Fig. 9. One can see that LOVA 3 is capable of asking versatile questions based on the content of the unlabeled images. Such results demonstrate the potential of the current MLLM to actively ask questions. We would like this finding to inspire future works that explore human-AI interaction in depth.

D EvalQABench

Why Fuyu-8B and Llama 2? To build the EvalQABench, we used two open-source models, Fuyu8B and Llama 2, to 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 are 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 2 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 is 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 99,998 valid outcomes, and then we use manual filtering to remove 38,904 samples. We conduct error correction to 14,814 samples and then pass 61,094 samples to Llama 2 for feedback generation. After that, we adopt manual filtering to remove 19,502 samples with incorrect formats. The filtering of feedback includes overlength output or none of output.

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 can 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 to 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 in real-world scenarios. We also provide the statics 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 a 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-frequency 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.

Statistic

Number

Proportion

Total Questions

32,000

-

- Object

2,418

7.55%

- Yes/No

6,804

21.26%

- Counting

4,880

15.25%

- Color

3,756

11.73%

- Attribute

343

5.67%

- Number

1,814

1%

- Relation

2,380

7.44%

- Action

1,274

3.98%

- Other

8,331

26.03 %

Others

26.7%

Yes/No

20.8%

Counting

15.3%

Color

11.8%

Object

7.61%

Relation

7.44%

Attribute

5.31%

Action

3.99%

Number

1.07%

3

lot

shadom

100

mirror

computer

offic

eating

several

wh1

banana

Idir

te

kiteclck

plain

stop

Yes

dog

nothing

0

utrips

pizzat

green

bedroom

ubluel

play

human

glass

zebra

outside

right

coffe

urfing

lots

Living

pink

purple

horse

FDO

standing

0

shirt

round

e

luggase

grass

otop

P

apple

phone

happy

noon

ue

win

wood

lbox

tollet

gray

chickon

VT

cald

10

paper

pocing

skateboard

cat

bl

Londoni

food

clear

ac

kitchen

yellow

burtto

broccolitable

No

bikoe

young

giraffe

ed

none

baby

E Failure cases of EvalQABench

In this section, we show some failure cases of our LOV A 3 . 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 -90 ◦ and an incorrect OCR reference is provided, our LOVA 3 fails to give the correct time. The second example highlights the current limitations of LOVA 3 in mathematical calculations and multi-step reasoning. In the third example, LOVA 3 fails to correctly interpret the window of the living room, resulting in an incorrect answer. It is important to note that due to the limited text-centric instruction tuning datasets and mathematic relevant task-specific data in the current experimental settings, LOV A 3 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.

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Length of Feedback

0

1000

2000

3000

4000

5000

6000

Count

woman

photo

animal

dog

cat

room

image

sign

shirt

train

table

bus

plate

water

question

wall

tree

car

clock

number

plane

food

boy

hand

girl

horse

bird

pizza

light

truck

Nouns Category

Count (log scale)

hold

show

make

sit

help

play

stand

know

let

take

locate

eat

look

see

hope

use

contain

say

ride

go

walk

fly

picture

appear

hang

read

include

call

cover

write

Verbs Category

2×10 2

3×10 2

4×10 2

6×10 2

Count (log scale)

Answer

: The dog is laying down and resting its

head on a cushion or a brown pillow.

Question

: What is the dog doing in the

image?

Answer

: The dog in the image is jumping in

the air to catch a frisbee in its mouth.

Answer : The cat is sitting on a bed next to a pillow.

Answer : He is either showing the frog to someone or is about to let the frog jump off the plate.

Answer

: The man in the image is

wearing a blue jacket and a helmet.

大

Answer

: The woman in the image is skiing,

not snowboarding.

Answer

Image:

2

寸

D

8

10

Question:

What is the time? \Reference OCR token: N, u, g0 \Answer the question using a single word or phrase.

Answer:

10:10

Image:

Gasoline

Self Serve

3.49号

Regular

3.55

Plus

3.65号

Supreme

Diesel

3.60号

No.2

Question:

How many gallons of supreme gasoline can I get with $50?

Answer:

With $50, you can get approximately 110 gallons of supreme gasoline, as indicated by the gas station sign in the image.

TERRACE

21'6"×8'0"

DINING AREA

KITCHEN

CL

.2xb.8

SECOND BEDROOM

10'10° × 14'7*

MASTER BEDROOM

12'10” × 17'9"

9'10”×13'0"

DW

米

WC

Built-in

5'2° x 5'2*

CL 2'11°× 6'11

LIVING ROOM

BATH

P

14'6"×20′5”

FOYER

6′0° x 6'34”

Entrance

Forillustration purposes only

Image:

Question:

In which direction does the living room window face?

Answer:

The living room window faces the dining area.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: In abstract and introduction sections, we provide the clear motivation, the problem we are solving, and the contributions of this study.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We provide the limitation discussion in the conclusion section.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [No]

Justification: This study does not involve the theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We detailly provide all the training data and implementation details for reproducibility. Additionally, we provide the training and evaluation codes in the supplemental material.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: We provide the training and evaluation codes with new proposed data of EvalQABench in the supplemental material.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We provide the experimental settings and details in the experiments section.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: We provide the details of the train/test split in the main paper and hyperparameters in the appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: All experiments were done on 8 A100 (40G) GPU setup.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: The research of this paper conform the Code of Ethics of NeurIPS.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We provide the positive impacts in the appendix while there are no negative impacts of our work.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: This paper has no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: The code, data, and models we used in this study follow the corresponding license.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We create a new dataset that follows the license CC-BY 4.0.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: There are no human subjects involved in our study.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: There are no human subjects involved in our study.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\nThe 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.\n\nWEAKNESSES:\nAs 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.\n\nQUESTIONS:\nIn 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\".\n\nLIMITATIONS:\nSmaller 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).", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n- 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.\n\nWEAKNESSES:\n- 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.\n\nQUESTIONS:\n(Table 6) Why does adding GenQA-Grounding data improves ScienceQA performance?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-14"}