LOVA3: Learning to Visual Question Answering, Asking and Assessment

Authors: henry  hengyuan zhao
pan  zhou
difei  gao
zechen  bai
mike zheng shou
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 09:51:24

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.

Main paper: 
 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] . 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:(1) 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.(2) We build a new benchmark EvalQABench for the VQA evaluation as the first effort to advance the VQA data assessment of future research.(3) The experimental results demonstrate that training with LOVA 3 consistently improves performance across several multimodal benchmarks, including VQAv2, GQA, MME, VizWiz, MMBench, and MM-Vet, etc.2 Related Work 
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.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.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. 100K Note: randomly choose from 58 instruction prompts GQA [30] 100K Example: Can you provide a clear question and its answer based on the image? OCR-VQA [56] 80KCounting20K † 20K LLaVA-250K † [43] 250KMulti-choice VQA A-OKVQA [71] 17K Can you provide a clear question and its answer based on the image?\nThis is a Multi-choice VQA task.Multi-turn VQA VQAv2 [26] 83K Design a conversation between you and a person asking about this photo.GQA [30] 72KThe answers should be in a tone that a visual AI assistant is seeing the image and answering the question. Ask diverse questions and give corresponding answers.VG [33] 30K Note: randomly choose from 58 instruction prompts with a specific task description prompt.Can you review the image and articulate a concise question and its answer?\nThis is a Referring Expression Comprehension (REC) task. The question will express a specific region of the image.Please provide the coordinates in the answer.REG VG [33] 30K Note: randomly choose from 58 instruction prompts with a specific task description prompt.RefCOCO [31] 30KCan you review the image and articulate a concise question and its answer?\nThis is a Referring Expression Generation (REG) task. The purpose of REG is to generate a unique description for a specified location.Total -842K 
methodology: 
 In this section, we introduce LOVA 3 , a new framework designed to imitate two essential abilitiesasking 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.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.Completing the VQA assessment often requires fine-grained and deep visual understanding. As emphasized in Sec. 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: No, the people in the picture are motorcycling.No, the second sign says "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.\nHere, <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. 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.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 LLaVA-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.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 :EQUATIONwhere X T represents the input text. X T can be an example of the three types: 1) VQA data, e.g., "What color is the pot?"; 2) GenQA data like "Can you provide a concise question and answer based on the image?"; 3) EvalQA data, such as "What kind of flowers are on the picture to the left?\nAnswer: pansy. \nPlease examine the correctness of this question and answer according to the image content. Output Yes or No with the feedback". Accordingly, the input instruction template can be unified into the following ones:"question": "What is the cat lying on?", "answer": "table", "fuyu_answer": "cat""question": "Who is in the photo?", "answer": "man", "fuyu_answer": "man""question": "Is this a little boy's or little girl's room?", "answer": "boy", "fuyu_answer": "no""question": "How many sinks?", "answer": "2", "fuyu_answer": "two""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 
USER:X I X T \n ASSISTANT: X R 
We follow previous MLLMs [42, 17] , and design the training objective in an autoregressive manner:EQUATIONwhere x i is the current prediction token and L denotes the response sequence length. θ denotes the trainable parameters. 
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 LOVA 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.Table 3 : Results on five generic tasks including VQAv2 [26] , GQA [30] , VizWiz [27] , ScienceQA [50] , and POPE [39] . The first two columns represent the results on held-in datasets marked as * , and the last three columns represent the held-out datasets. The best result on each subtask is bolded. 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 LOVA 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 LOVA 3 -1.5B and LLaVA-Phi-1.5, one can see a greater improvement than LOVA 3 -7B. 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-Flash1 as both the MLLM and LLM in Fig. 2 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. 
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  
broader impacts: 
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 Appendix: 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.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. Generic VQA. It includes four datasets: VQAv2 [26] , GQA [30] , OCR-VQA [56] and Count-ing110K [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] Table 9 : 665K instruction data of LLaVA1.5. The content is from LLaVA1.5 for convenient browsing.LLaVA [43] 158K -ShareGPT [74] 40K -VQAv2 [26] 83K Answer the question using a single word or phrase. GQA [30] 72K OKVQA [54] 9K OCRVQA [56] 80KA-OKVQA [71] 50K Answer with the option's letter from the given choices directly.TextCaps [75] 22K Provide a one-sentence caption for the provided image.RefCOCO [31, 53] 30K Note: randomly choose between the two formats Provide a short description for this region.VG [33] 86K Provide the bounding box coordinate of the region this sentence describes.Total 665K 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.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.Why Fuyu-8B and Llama 2? To build the EvalQABench, we used two open-source models, Fuyu-8B 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.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. In this section, we show some failure cases of our LOVA 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, LOVA 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. Count (log scale) 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).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.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.https://deepmind.google/technologies/gemini/flash/ 


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

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

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

**R1**

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

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

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

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

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

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

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

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


**R2**

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

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

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

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

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

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

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

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

Reviews: review: paper_summary: paper_summary: The paper introduces LOVA3, a framework designed to enhance Multimodal Large Language Models (MLLMs) by incorporating not only visual question answering (VQA) but also the capabilities of generating questions (GenQA) and evaluating question-answer pairs (EvalQA). The primary objective is to improve the comprehensive multimodal understanding of AI models. 

LOVA3 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.
strength_weakness: strength_weakness: 1. Incorporating additional tasks like GenQA and EvalQA, but the two tasks are also the existing steps of the visual language instruction generation for visual question answering (e.g. SEED-Bench) or visual instruction tuning (e.g., LLaVa-Bench). They also used LLMs or MLLMs for the dataset generation and validation. To explained the special novelty or contribution would be better.
2. 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.
3. The paper lacks a more in-depth analysis of potential data biases and strategies to mitigate them.
4. 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.   
5. The paper does not provide the generated dataset for review, which is important for the validation of the work.
questions: questions: 1. How about the prompt stability of the QA generation and the differences when using the different variants of prompts?
2. Why does the work apply Fuyu-8B instead of LLaVA 1.5 for the data generation and is there any comparison between the different new VLMs?
limitations: limitations: NA
paper_summary: paper_summary: This paper enhances the MLLM's visual understanding capability by training it to ask questions about an image and evaluate the correctness of given question-answer pairs about an image. To achieve this goal, new data is extracted from existing datasets and a new model is fine-tuned on the new data. The experiment shows that the newly added data can improve the MLLM's capability of understanding of images with higher scores on VQA tasks.
strength_weakness: strength_weakness: - 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.

- 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.

[1] Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.
questions: questions: (Table 6) Why does adding GenQA-Grounding data improves ScienceQA performance?
limitations: limitations: The paper mentions some limitations of the proposed pipeline. However, as mentioned above, the biggest limitation is the limited scope of the considered setting which only involves VQA (including grounding) problems without considering the embodiment of MLLMs.
paper_summary: paper_summary: This paper augments presents a data augmentation / multi-task learning technique to improve model quality for Visual Question Answering (VQA). The key idea of the paper, motivated by analogy to humans, is that asking questions and assessing answers are also key skills, apart from just answering questions. The paper seeks to train a model to "Answer, Assess, Ask" jointly, relying existing datasets for a variety of answering and asking tasks, and deriving a new dataset called EvalQABench for the assessment task. The introduction of the EvalQABench dataset, initially created by LLMs and later filtered by experts, is another potentially valuable and lasting contribution. Multiple tasks on augmented data are implemented on the LLava backbone which is an existing SOTA model. The paper compares their technique (called LOVA) to several SOTA models on a variety of datasets showing robust gains in a multitude of settings, providing confidence in the technique's validity.
strength_weakness: strength_weakness: As the authors point out, due to cost considerations, the authors only evaluate the technique on smaller (relative to MLLMs) models. This is important as model size is a confounder when it comes to assessing the usefulness of data augmentation or multi-tasks. A technique useful for a 7B model is not necessarily useful for a 70B model. However, given the cost of inference of larger models, improving smaller models to be competitive with larger models has its own benefit. 

There 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.
questions: questions: In section 1, there's a claim that the EvalQABench datasets is generated via a "new automatic pipeline". However, in section 3.2 the authors say "... acknowledging that the Fuyu-8B model is not flawless and recognizing that no multimodal model, including GPT-4V, is perfect, we have implemented both manual filtering and error correction...". Do the earlier claims about the pipeline being automatic overstate the case? Are they necessary? 

Does the feedback add value beyond just rephrasing the answer is a longer sentence. A lot of the feedback seems trivial and already captured in the q,a pair. For e.g. "What does the woman have on her back". "backpack" vs "No, the woman has a backpack on her back".  As another e.g. "What are the people doing?", "motorcycling", vs "No, the people in the picture are motorcycling".
limitations: limitations: Smaller models sizes are an understandable limitation as mentioned in the paper, and referenced earlier in the review.

The 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).

Comments: title: Author Response to Reviewer 4XZr
Author Response to Reviewer 4XZr
Author Response to Reviewer UkuU
Please read the author rebuttal, other reviews and respond to the authors NOW!
Official Comment by Authors
Official Comment by Authors
Response to Reviewer oUEn
Response to Reviewer nUFy
Author response to all reviewers
comment: Thanks for the response. I understand that your focus is on multimodal foundation model training, but if your method is general and most likely to be useful for some specific task (here interactive agents), the evaluation should better reflect this point.  I am not saying that the proposed method is useless, but that the paper can be largely improved and put into a more appropriate context if evaluation settings change from VQA to interactive tasks. The authors are encouraged to do so in order to make the paper much stronger to have a larger impact on the community.

On the other hand, the authors said that "Adapting to these tasks would require entirely different **methods** and experiments", but if your method is to improve the general capability of foundation models, why do you need entirely different **methods** when considering that the improved capability is closely related to the tasks? 

Considering that the authors have clarified some of the points, I raise the score to 4.
Dear Reviewer 4ZzR,

Firstly, there is no need to change our current settings. **There is no clear evidence to suggest that asking and assessing are exclusively applicable to the interactive environment.** And then, we demonstrate that incorporating the two additional tasks is **benefit** for MLLM training. The tasks, such as embodied AI and GUI Assistant, are **downstream tasks**, whereas our focus is on **multimodal foundation model training**. Adapting to these tasks would require **entirely different methods and experiments, essentially resulting in a separate paper**. It is important to clarify that it is not inappropriate to focus on downstream tasks before evaluating the approach on a foundation model.

Secondly, **we referred to previous works to underscore the significance of multimodal instruction tuning and to provide evidence supporting the validity of evaluating on VQA datasets and benchmarks.** These examples serve as the foundation of our argument. We respectfully disagree with your viewpoint, particularly since the **other two reviewers found our experimental setup to be reasonable, fair, and insightful.**

Thirdly, as shown in Table 7, we have already evaluated the assessing ability in comparison with other SOTA models. The results clearly demonstrate that our model significantly enhances this ability without introducing prediction bias. Our evaluation setting was conducted reasonably.

Best regards,

Authors of Paper 9008
Thanks for the response. 

Regarding your first, second, and third arguments, other agents, like GUI Assistant/digital-device agent and LLM-Agent still need an interactive environment in the loop, so you still need to change your evaluation settings. My opinion is not restricted to robotics tasks. It is not appropriate to say that "previous works use VQA tasks for evaluation, so we use them as well"; a good paper should choose/create the most appropriate settings that can validate the main claims.

I keep the score.
Dear Reviewer 4XZr,

We deeply appreciate the time you have devoted to reviewing our manuscript. In our rebuttal, we have carefully considered and addressed your concerns. As we have not yet received your feedback during the discussion period, we would like to summarize our rebuttal below to help clarify the key points.

Regarding your main concerns about evaluating the answering, asking, and assessment capabilities on generic multimodal tasks rather than embodied tasks, we would like to express our viewpoint:

- **Multimodal instruction tuning is a crucial and foundational area that can significantly benefit downstream tasks, including embodied tasks and GUI assistance.**

- **We would like to emphasize that evaluating MLLMs on VQA tasks is a widely accepted practice, as demonstrated by other models such as LLaVA1.5, Qwen-VL, and CogVLM. This evaluation is essential to assessing the overall capabilities of MLLMs.**

- **Applying questioning and assessment abilities is equally important in both general MLLM research and embodied AI research. Moreover, our experiments reaffirm that adding the two additional abilities is able to bring consistent and robust gains. Therefore, conducting research with MLLMs tested on VQA tasks is a non-trivial and essential endeavor.**

We trust these responses effectively address the concerns you raised. As the discussion period deadline approaches, we eagerly await any further feedback you may have.

Once again, thank you for your dedication to the review process.

Best regards,


Authors of Paper 9008
Dear Reviewer UkuU,

Thank you again for your valuable reviews of our submission. As we have not yet received your feedback on our rebuttal in the current discussion period, we would like to summarize our key points below to help address your concerns.

- **Regarding the use of LLMs or MLLMs in creating the EvalBench, we believe this approach is non-trivial and essential for training MLLMs effectively.** We would like to clarify that, unlike SEED-Bench and LLaVA-Bench, we did not use commercial models like ChatGPT for data generation. **It brings insights for other future works for their data generation with low financial costs.** Additionally, **current published works LLaVA [1] (NeurIPS 2023), Ferret [2] (ICLR 2024), SNIFFER [3] (CVPR 2024), Next-GPT [4] (ICML 2024), ShareGPT4V [5] (ECCV 2024) also use GPT-4(V) to build their corresponding data** for training specialized MLLMs. It is the common practice of using stronger LLMs/MLLMs for data generation.

- **While we respectfully disagree with the viewpoint that 'LLaVA1.5 is stronger than Fuyu-8B,'** we acknowledge that LLaVA1.5 achieves exceptional performance on diverse datasets and benchmarks. However, this does not necessarily indicate that it is superior to Fuyu-8B, as LLaVA1.5 was trained on foundational and relevant datasets like VQAv2 and GQA. Therefore, it is reasonable to use Fuyu-8B to generate negative answers while the results in Table 6 demonstrate that our synthetic data is high quality which is challenging for SOTA MLLMs.

- Adhere to the policy of NeurIPS, we create an anonymous link **https://anonymous.4open.science/r/LOVA3-9008/README.md that includes all our training and evaluation codes, created datasets, and pre-trained weights**.

We hope these clarification adequately address your initial questions. As the discussion period deadline approaches, we keenly await your further feedback you may have.

Once again, thank you for your dedication to the review process.

Best regards,

Authors of Paper 9008

---

References:

[1] Visual Instruction Tuning

[2] Ferret: Refer and Ground Anything Anywhere at Any Granularity

[3] SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection

[4] NExT-GPT: Any-to-Any Multimodal LLM

[5] ShareGPT4V: Improving Large Multi-modal Models with Better Captions
Dear Reviewers,

Thanks to those of you who already responded to the authors acknowledging the rebuttal and asking follow-up questions if any.

Those who have not responded yet, please do the following ASAP: thoroughly read the rebuttal, the other reviews and respond to the authors about whether all your questions / concerns have been addressed or not. If not, please elaborate on which questions / concerns are still not addressed so that the authors have fair chance of addressing them before the author-reviewer discussion period ends in ~41 hours from now (August 13th, 11:59pm AoE).

Your AC
Dear reviewer, we greatly appreciate your time in reviewing our response. We hope that your concerns are addressed with our rebuttal. Please let us know if there are any further questions that need clarification.
Dear reviewer, we would like to thank you for your insightful feedback. We hope that your questions are addressed with our rebuttal. Please let us know if there are any further questions that need clarification.
Dear Reviewer oUEn,

We sincerely appreciate you taking the time to review the ethical aspects of our submission.

Warm regards,

Author(s)
Dear Reviewer nUFy,

Thank you for taking the time to evaluate the ethical aspects of our work. We appreciate the reviewer’s concern regarding potential biases in our dataset and the ethical implications of our labeling process. We fully acknowledge the importance of addressing these issues to ensure the fairness and reliability of our results. We believe there may have been some misunderstandings, which we address in the following explanations.

The first misunderstanding of our work we would like to clarify is that the EvalQABench is only for the EvalQA task which is adapted from the existing dataset VQAv2. There are no new datasets proposed for the GenQA task. In Table 1, the datasets listed are used for training the GenQA task with their original annotations. The construction of EvalQABench adheres to the VQAv2 dataset’s license (CC-BY 4.0).


Bias: To mitigate biases, we randomly selected original VQA samples that exhibit no apparent biases from VQAv2. We are committed to transparency and have ensured that our methods align with NeurIPS ethical standards.

Annotation Details: During the manual filtering stage, we asked two participants to summarize the error patterns in the generated negative answers and feedback. We then wrote code to automatically filter or correct these error samples. Throughout this process, we adhered to NeurIPS ethical standards, ensuring that no potential ethical issues were introduced. We will include details of annotation in our next version to provide further clarification.

Thank you again for your valuable reviews of our submission. Please let us know if there are any further questions that need clarification.

Warm regards,

Author(s)
**We sincerely thank all the reviewers for their valuable time and their thoughtful comments and questions. We are encouraged that the reviewers find that**:

- We introduce LOVA$^3$, which enhances MLLM training by incorporating two additional capabilities: question-asking and assessment (UkuU, 4XZr, 5aGT), drawn from human learning mechanisms. This approach is **insightful** (UkuU), **reasonable** (4XZr), and **well-motivated** (5aGT).
- We have developed a novel benchmark for evaluating the accuracy of VQA triplets, featuring carefully selected question types that **bring valuable and lasting contributions** (5aGT). The creation of EvalQABench provides a **rigorous way to test and improve MLLMs** (UkuU).
- We evaluate LOVA$^3$ against a variety of SOTA models across multiple tasks, demonstrating **robust improvements** (UkuU, 4XZr, 5aGT). The results offer **valuable insights** (UkuU) and are **reasonable** (4XZr). The ablation studies for various components **showcase their effectiveness** (5aGT). The experimental settings are **carefully designed and fair** (4XZr).


To better review the details of the proposed EvalQABench and validate the reproducibility of this work, **we create an anonymous link at **https://anonymous.4open.science/r/LOVA3-9008/README.md** following the double-blind policy**. This link contains all the datasets we created for EvalQABench, as well as the training and evaluation codes and model weights. Please refer to it for further details.


Finally, **we would like to emphasize again that our intention with this paper is to advance the multimodal instruction tuning of current MLLMs by introducing two intuitive and reasonable tasks: GenQA and EvalQA along with traditional VQA.** These additions are designed to enhance problem-solving capabilities. By focusing on these three tasks, **we demonstrate significant improvements across diverse benchmarks and offer insights** that can inspire further exploration and development in the field of multimodal instruction tuning. We trust that **our contributions will be thoroughly evaluated by the reviewers, as this training paradigm is genuinely novel** within the current multimodal instruction tuning domain.

We attempted our best to address the questions as time allowed. We believe the comments have made the paper stronger and thank all the reviewers for their help. Please find individual responses to your questions below.


### References:

[1] SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

[2] Visual Instruction Tuning

[3] Fuyu-8B: A Multimodal Architecture for AI Agents

[4] Llama 2: Open Foundation and Fine-Tuned Chat Models

[5] Improved Baselines with Visual Instruction Tuning

[6] VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization

[7] CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization

[8] Open-Vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering Models

[9] STAR: A Benchmark for Situated Reasoning in Real-World Videos

[10] SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge

[11] CinePile: A Long Video Question Answering Dataset and Benchmark

[12] AssistGUI: Task-Oriented Desktop Graphical User Interface Automation

[13] MEDIQ: Question-Asking LLMs for Adaptive and Reliable Clinical Reasoning

[14] Reflexion: Language Agents with Verbal Reinforcement Learning

[15] CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

[16] Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

[17] CogVLM: Visual Expert for Pretrained Language Models

[18] MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

[19] MMBench: Is Your Multi-modal Model an All-around Player?

[20] MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

[21] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering