Venue: NAACL 2025

# Bridging the Visual Gap: Fine-Tuning Multimodal Models

with Knowledge-Adapted Captions

Moran Yanuka\({}^{\tau}\) Assaf Ben Kish\({}^{\tau}\) Yonatan Bitton\({}^{G}\) Idan Szpektor\({}^{G}\) Raja Giryes\({}^{\tau}\)

###### Abstract

Recent research increasingly focuses on training vision-language models (VLMs) with long, detailed image captions. However, small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-tuning. In this paper, we explore how well VLMs adapt to such captions. To quantify caption quality, we propose Decomposed NLI (DNLI), an evaluation framework that breaks down generated captions into individual propositions, assessing each in isolation. This fine-grained analysis reveals a critical balance between capturing descriptive details and preventing hallucinations. Our findings show that simply reducing caption complexity or employing standard data curation techniques does not effectively resolve this issue. To tackle this challenge, we introduce Knowledge Adapted (KnowAda) fine-tuning, a data-centric approach that automatically adapts training data with the model's existing knowledge and visual understanding. KnowAda minimizes hallucinations while preserving high descriptiveness. We validate this approach across several small-scale VLMs (up to 7B parameters) and dense caption datasets, demonstrating that KnowAda effectively balances hallucination reduction and descriptiveness. Our results show that KnowAda outperforms various baselines in both automatic metrics and human evaluations. The code is available here.

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in capturing fine-grained visual details during finetuning, resulting in hallucinations.

Consider a fine-tuning dataset for dense captioning, like the one in Figure 1 from DOCCI Onoe et al. (2024), alongside a pretrained vision-language model trained on tasks like captioning, VQA, and OCR. If, for instance, the model has only encountered low-resolution images during pretraining, it may struggle to identify fine details, such as the drawing on the purple van or the hotel name in the background, which require higher resolution. This issue extends beyond resolution to other visual challenges in modern VLMs Tong et al. (2024); Wu et al. (2024); Zhang et al. (2024). We hypothesize that fine-tuning the model on overly complex captions may increase hallucinations, as the model is compelled to predict details it cannot accurately perceive or understand.

Recent works on large language models (LLMs) have shown that fine-tuning primarily adapts preexisting factual knowledge for specific tasks, with the majority of this knowledge being encoded during the pretraining phase Geva et al. (2020); Meng et al. (2022); Roberts et al. (2020). Rather than acquiring new information, fine-tuning typically _activates and refines pretrained knowledge_, which remains largely stable throughout the process Zhou et al. (2024). Furthermore, Gekhman et al. (2024) demonstrated that fine-tuning on content not grounded in a model's pre-existing factualknowledge can lead to an increase in hallucinations. Similarly, Yu et al. (2024) showed that attempting to distill GPT4V into a smaller, less capable VLM significantly increases hallucinations. Motivated by these findings, we hypothesize that one possible cause of increased hallucinations is the excessive visual complexity of captions relative to the model's pretrained capabilities. We propose a method to mitigate this effect.

To better adapt pretrained models to dense caption datasets, we introduce KnowAda, a model-specific adaptation technique that simplifies complex details in dense captions. The KnowAda pipeline, shown in Figure 2, automatically identifies visual knowledge gaps between a pretrained VLM and an image-caption pair by generating questions related to the image's visual content. It then modifies the captions to align with the model's visual knowledge and capabilities, producing adapted captions that enhance fine-grained control while balancing a low hallucination rate with high descriptiveness.

Evaluating dense image captions requires attention to two critical factors: descriptiveness, which captures the image's details, and hallucination rate, which measures factual accuracy. Traditional metrics, such as those based on similarity to reference captions Papineni et al. (2002); Banerjee and Lavie (2018);

Figure 2: **Our proposed KnowAqa pipeline. We first probe the knowledge of the VLM, identifying the known and unknown parts of the image description, by generating questions about the visual content of the image mentioned in the caption. Then, KnowAda identifies the knowledge gaps by judging the VLM answers to these questions. Finally, KnowAda adapt the description to match these gaps (e.g., removing the number of limousines mentioned in the caption, which relates to a question the model failed to answer).**



2005; Zhang et al., 2019; Reimers, 2019) or CLIP-based alignment (Sarto et al., 2023; Radford et al., 2021), often fall short when applied to long captions. These approaches penalize valid variations in phrasing and fail to distinguish between factual accuracy and token overlap or semantic similarity, rendering them inadequate for identifying hallucinations in detailed captions. Existing hallucination metrics are similarly limited, focusing primarily on short captions or object-level errors (Rohrbach et al., 2018; Li et al., 2023c; Ben-Kish et al., 2024), or relying on other VLMs (Jing et al., 2023; Liu et al., 2023), which themselves can hallucinate during the verification process. To address these limitations, we propose Decomposed NLI (DNLI), a novel evaluation framework that breaks captions into propositions and assesses their entailment with the detailed ground truth description. DNLI offers a more reliable measure of both descriptiveness and accuracy, demonstrating strong alignment with human judgments.

Our results show that training with KnowAda captions offers a favorable balance between descriptiveness and fidelity when fine-tuning LLaVA-7B, outperforming other data-centric baselines. To demonstrate the consistency of KnowAda across different models and datasets, we fine-tuned several multimodal models, ranging from 2 billion to 7 bilion parameters, on two different dense captioning datasets. Across all models, KnowAda consistently reduced hallucinations compared to training with the original captions, as confirmed by both automatic and human evaluations.

Stated explicitly, our contributions are: (I) We show that small-to-medium-scale VLMs underperform when fine-tuned directly on dense caption datasets, exhibiting increased hallucinations and reduced descriptive accuracy; (II) To address this we propose KnowAda, a model-dependent augmentation method that fills knowledge gaps in captions, reducing hallucinations while preserving high descriptive accuracy; (III) We demonstrate the effectiveness of KnowAda through extensive experiments, supported by both quantitative metrics and qualitative human evaluations; (IV) We introduce DNLI, a novel evaluation framework for dense captions that offers a more fine-grained analysis and shows strong correlation with human annotations.

## 2 KnowAda

We begin by introducing our caption adaptation method for dense captioning datasets. KnowAda comprises three stages, as shown in Figure 2. First, we use an LLM to generate visual questions from each dense caption. Next, these questions are employed to probe the VLM's pretrained visual knowledge and identify parts of the image caption the model struggles with. Finally, an LLM adapts the image descriptions by editing out the unknown parts. Below, we elaborate on each step of the pipeline.

### VLM Knowledge Probing

In order to detect the visual attributes of the image that are unknown to the VLM but are mentioned in the image description, we probe the VLM knowledge. It is done by generating visual questions which can be answered by the image description, then letting the VLM answer these questions, and finally measuring if the responses are correct. We provide further details regarding each stage below.

#### Finding the unknown questions.

Given a dataset \(D\) containing image descriptions, we aim to find all the parts of the description that are unknown to the VLM. Following prior work on uncertainty estimation (Cheng et al., 2024; Gekhman et al., 2024), the steps are as follows:

Figure 3: **Dense captioning descriptiveness precision-recall results for LLaVA-7B fine-tuned with DOCCI captions, adapted using different methods. “Trimmed” refers to naive removal of sentences, while “Gemini” involves prompting Gemini to simplify the caption by removing difficult details of varying degrees. KnowAda consistently achieves better precision-recall balance. Original captions corresponds to KnowAda with a threshold of \(T=100\%\), where no information is classified as unknown.**



1. For each image caption \(d\in D\), generate \(n\) questions \(Q=\{q_{1},q_{2},\ldots,q_{n}\}\). Note that \(n\) is caption dependent (e.g., more questions are generated for longer captions).

2. For each question \(q_{i}\in Q\), sample \(m\) answers. Let \(A_{i}=\{a_{i1},a_{i2},\ldots,a_{im}\}\) represent the set of \(m\) answers for question \(q_{i}\).

3. Evaluate each answer \(a_{ij}\in A_{i}\) to determine how difficult it is for the specific VLM. Let \(C_{i}\subseteq A_{i}\) be the set of correct answers and \(I_{i}\subseteq A_{i}\) be the set of incorrect answers for question \(q_{i}\).

4. Calculate the difficulty of each question \(q_{i}\) based on the ratio of incorrect to incorrect and correct answers. The difficulty score \(df_{i}\) for question \(q_{i}\) is given by:

\[df_{i}=\frac{|I_{i}|}{|C_{i}|+|I_{i}|}\]

Here, \(|C_{i}|\) and \(|I_{i}|\) are the number of correct and incorrect answers respectively. A higher value of \(df_{i}\) indicates a more difficult question. For a threshold \(T\), a question with \(df_{i}>T\) is defined as an _unknown question_.

We assess the accuracy of the model's responses by prompting a LLM to evaluate each generated answer in relation to the given question and the ground truth description.

For example, consider the two questions shown in Figure 2 (ii). If the model correctly answered "Monozil" for the question "What kind of vehicle is visible in the image?" in 6 out of 10 sampled instances, with 4 incorrect answers, the difficulty for this question is calculated as \(df=\frac{4}{10}\) . For the second question, "How many limousines are parked underneath the track?", the model correctly answered "3" only 4 out of 10 times, resulting in a difficulty of \(df=\frac{6}{10}\). With a threshold of \(T=50\%\), the first question is classified as known, while the second as unknown. However, if the threshold is raised to \(T=70\%\), both questions would be considered unknown.

For example, consider the two visual questions shown in Figure 2 (ii) with the following prediction accuracies:

* For the question "What kind of vehicle is visible in the image?", the model correctly answers "Monozil" in \(6/10\) instances, resulting in \(df=4/10=0.4\).
* For the question "How many limousines are parked underneath the track?", the model correctly answers "three" in \(4/10\) instances, yielding \(df=6/10=0.6\).

With a difficulty threshold \(T\), the classification of these questions changes:

Figure 4: **DNLI Evaluation**. Given a generated description by a VLM, we decompose it to atomic propositions. Then, we classify each proposition to either entailed, contradicted or neutral, conditioned on the ground-truth description. Finally, we calculate the descriptiveness and contradiction based on the number of entailed and contradicted propositions.

 

1. For \(T=50\%\): * The first question is Known (since \(df=0.4<0.5\)) * The first question is Unknown (since \(df=0.6>0.5\))
2. For \(T=70\%\): * The first question is Unknown (since \(df=0.4<0.7\)) * The second question is Unknown (since \(df=0.6<0.7\))

**Image description rewriting.** After identifying the questions unknown to the model for each image description, we prompt a LLM, namely, Gemini with several in-context examples to remove or edit the parts of the caption that answer these unknown questions, while keeping the other details intact. For instance, in Figure 1, the questions about the number and type of cars parked in the parking lot is unknown to the small VLM. Therefore the corresponding questions are passed to Gemini, which edits "three limousines" to "vehicles".

Formally, for each image description \(d_{i}\), we use all the corresponding questions classified as unknown to the VLM according to the threshold \(T\), denoted as \(Q_{i}=\{q_{i,1},q_{i,2},\ldots,q_{i,\bar{n}}\}\), to prompt the LLM to rewrite the description by removing information associated with \(Q_{i}\). See Appendix A.1 for the prompts used at each stage.

### KnowAda Data Analysis

We start by examining the characteristics of the dense captioning datasets, the impact of KnowAda on the adapted captions, and the types of questions utilized for visual probing within the KnowAda pipeline.

**Datasets.** We use two variations of dense caption datasets in our experiments: DOCCI Onoe et al. (2024), a human-annotated dataset rich in visual details, and DOCCI images paired with synthetic captions generated by Gemini-Pro-1.5, which are designed to be highly visually descriptive. These datasets differ in caption style and level of visual detail, allowing us to demonstrate the robustness of KnowAda across varying data distributions.

**Dataset characteristics.** Figure 6 illustrates the overlap of unknown questions across the different models. While there is a core set of unknown questions common to all three models, each model also has its own unique set of unknown questions.

Table 2 presents statistics for each dataset, including the average number of unknown questions per model and dataset, as well as the average word count in both the original and KnowAda captions.

\begin{table}
\begin{tabular}{l l|c c|c c|c c|c c|c} \hline \hline  & & \multicolumn{3}{c|}{**Contradiction \(\downarrow\)**} & \multicolumn{3}{c|}{**Descriptiveness \(\uparrow\)**} & \\ \cline{3-9} Model & FT Captions & \multicolumn{2}{c|}{**Precision**} & \multicolumn{2}{c|}{**Recall**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c|}{**Recall**} & \# Words \\ \cline{3-9}  & & Auto & Human & Auto & Human & Auto & Human & Auto & Human & \\ \hline PaliGemma & Synthetic & 38.9 & 19 & 30 & 15.5 & 47.9 & 81 & **39.2** & **67.8** & 72 \\ PaliGemma & Synthetic KA & **32.4** & **18.4** & **20.8** & **13** & **55.2** & **81.6** & 36.4 & 61.2 & 54 \\ \hline TinyLAVA & Synthetic & 38.1 & 52.1 & 49.1 & 45 & 35.1 & 47.9 & **26.3** & 40.8 & 71 \\ TinyLAVA & Synthetic KA & **22.9** & **34** & **40.2** & **22.2** & **48.1** & **66** & 25.4 & **40.9** & 51 \\ \hline LLaVA-7B & Synthetic & 39.1 & 19.3 & 39.1 & 16.2 & 47.3 & 80.7 & **39.7** & **65.3** & 79 \\ LLaVA-7B & Synthetic KA & **31** & **15.8** & **31** & **9.3** & **58.4** & **84.2** & 34.5 & 47.4 & 54 \\ \hline PaliGemma & Human & 41.6 & 20.4 & 24.6 & 14.9 & 46.7 & 79.6 & 24.6 & **43.2** & 62 \\ PaliGemma & Human KA & **38.3** & **18.3** & **22.2** & **13.6** & **49.4** & **81.7** & **28.7** & 38.9 & 65 \\ \hline TinyLLaVA & Human & 53 & 51.8 & 39.5 & 38.8 & 31.9 & 51.8 & **22.4** & **31.4** & 100 \\ TinyLAVA & Human KA & **42.6** & **34.5** & **19.6** & **12.5** & **46.9** & **65.5** & 19.1 & 22.5 & 53 \\ \hline LLaVA-7B & Human & 47.2 & 33.4 & 39.7 & 33.2 & 39.4 & 66.6 & **33.7** & **48.1** & 109 \\ LLaVA-7B & Human KA & **33.7** & **17.1** & **16.7** & **11.2** & **56.9** & **82.9** & 25.8 & 31.8 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: **Dense captioning results over the test sets of DOCCI when fine-tuning on original human-annotated captions, synthetic captions, and KnowAda-adapted captions (denoted as KA) with a threshold of 20%. “Automatic (Auto)” refers to model-based NLI evaluation, while “Human” refers to evaluations based on human labeling.** 

The data indicates that the average number of unknown questions and the average word count in the rewritten captions are relatively consistent across different models, where the human-authored DOCCI captions containing slightly more challenging questions compared to the synthetic captions.

**Visual questions category distribution.** To verify that KnowAda generates diverse types of visual questions, we use LLaMa-3-70B Dubey et al. (2024), quantized to 4 bits, to classify 12,422 questions generated for 1,000 images from the DOCCI test set into categories defined by SeedBench Li et al. (2023). For the classification task, we provide one-few shot examples from each SeedBench category. The resulting distribution of questions is presented in Figure 5. As observed, the diversity in the question types suggests that KnowAda performs knowledge probing across a wide range of visual tasks.

## 3 Decomposed NLI Evaluation

Next, we introduce DNLI, a proposition-decomposition evaluation framework for dense-captioning. It evaluates the quality of a generated dense image caption using two criteria: _descriptiveness_, which measures accuracy and detail, and _contradiction_, which measures how much the caption contradicts the ground truth. Inspired by prior work on paragraph summarization evaluation Ernst et al. (2021); Zhang and Bansal (2021); Ernst et al. (2021) and retrieval Chen et al. (2023), DNLI assesses descriptiveness and consistency through proposition extraction, as outlined below and shown in Figure 4. Additional qualitative examples are in the appendix.

**Propositional decomposition.** Given a generated image description, we use a Gemini to decompose it into a set of atomic propositions, capturing individual, verifiable claims. This enables fine-grained evaluation. To avoid duplicates, we instruct the model to include only unique propositions.

**Natural Language Inference (NLI) analysis.** We next evaluate the entailment of propositions generated from the image using both our automatic method, where Gemini is used to compute textual entailment, and through human annotators who directly assess visual entailment:

#### Automatic Textual Entailment

Each atomic proposition is assessed with a Natural Language Inference (NLI) model. This model compares each proposition (the hypothesis) to a ground truth description of the image (the premise), determining whether the proposition is entailed, contradicted, or neutral with respect to the description. This evaluation forms the basis for our descriptiveness and contradiction metrics. See details and prompts in Appendix A.1.

#### Human-Based Visual Entailment Evaluation

We employ Amazon Mechanical Turk to engage three independent annotators for each proposition, tasking them with directly assessing its entailment based on the corresponding image. To ensure quality, we administer a qualification test and select the top-performing annotators. Each proposition is evaluated by a distinct set of three annotators, and the final entailment label is determined by a majority vote. For consistency, we randomly sample 20 images per model, generating an average of 175 propositions per model. In total, 2,812 unique propositions were evaluated. Further details are provided in Appendix A.3.

**Descriptiveness metric.** We quantify the _descriptiveness_ of the generated caption using two measures: recall and precision.

#### Descriptiveness Recall

The proportion of ground truth propositions that are entailed by the generated description:

\[\text{Descriptiveness~{}Recall}=\frac{|\text{Entailed}|}{|\text{Ground~{} Truth}|},\]

where the entailed propositions are those identified by the NLI model as described above.

#### Descriptiveness Precision

The proportion of entailed propositions relative to the total number of propositions in the generated description:

\[\text{Descriptiveness~{}Precision}=\frac{|\text{Entailed}|}{|\text{Generated}|},\]

where the generated propositions are those found in the model's caption. The precision represents the likelihood that a given proposition extracted from the generated caption, would be entailed.

**Contradiction metric.** The _contradiction_ precision and recall are calculated in a similar manner:

\[\text{Contradiction~{}Precision}=\frac{|\text{Contradicted}|}{|\text{ Ground~{}Truth}|}\]

\[\text{Contradiction~{}Recall}=\frac{|\text{Contradicted}|}{|\text{ Generated}|}\] 

Together, these metrics provide a comprehensive evaluation of the generated captions. Descriptive-ness precision and recall assess the accuracy and coverage of the content, respectively. Contradiction precision indicates the likelihood of a false proposition in a caption, while contradiction recall measures the total number of contradictions.

Note that the neutral labels are discarded in the automatic evaluation, as they could represent either subjective propositions (e.g., "vibrant atmosphere") or visual claims not described in the original captions. These labels could be either entailed or contradicted, but it is unclear which.

## 4 Experimental Settings

Our experiments concentrate on training dense captioning models. We evaluate the performance of models fine-tuned on captions curated through various methods against those fine-tuned on captions adapted using KnowAda.

We start by comparing KnowAda to data curation methods for multimodal datasets according to the DataComp challenge Gadre et al. (2024), specifically ICC Yanuka et al. (2024) and DFN Fang et al. (2023). We also compare our approach to the following baselines that might address the hallucination-descriptiveness trade-off:

* _Caption Trimming_: A progressive method that removes varying numbers of sentences to simplify captions.
* _Gemini Simplification_: An approach prompting Gemini to remove difficult details from captions to varying degrees.

For all methods in this experiment, we fine-tuned LLaVA-1.5-7B Liu et al. (2024) using Low Rank Adaptation Hu et al. (2021).

To ensure that KnowAda is robust across multiple models and datasets, we fix the threshold at 20% for classifying questions as unknown and fine-tune three models: PaliGEMma Beyer et al. (2024), TinyLLaVA Zhou et al. (2024), and LLaVA-1.5-7B Liu et al. (2024). We fine-tune on two DOCCI variations: one using the original DOCCI captions, and another using synthetically generated captions created by Gemini, which were prompted to be visually descriptive. We refer to Appendix A.4 for an experiment on the larger-scale PixelProse Singla

\begin{table}
\begin{tabular}{l c c c c} \hline \hline Captions & Model & \(\bar{C}_{o}\) & \(\bar{C}_{r}\) & \(\bar{Q}_{unk}\) \\ \hline Human & PaliGEMma & 122 & 84.2 & 5.5 \\ Human & TinyLLAVA & 122 & 80.9 & 6.3 \\ Human & LLaVA-7B & 122 & 80.7 & 6.3 \\ \hline Synthetic & PaliGEMma & 92 & 65.8 & 4.4 \\ Synthetic & TinyLLaVA & 92 & 63.3 & 5.2 \\ Synthetic & LLaVA-7B & 92 & 63.7 & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the original and KnowAda adapted captions with \(T=20\%\) over the human annotated and synthetic version of DOCCI captions. \(\bar{C}_{o}\) and \(\bar{C}_{r}\) denotes the mean number of words in the original caption and caption after applying KnowAda, and \(\bar{Q}_{unk}\) denotes the mean number of questions unknown to the model.

Figure 5: **Distribution of visual question categories** generated from captions in the first stage of KnowAda, classified using SeedBench Li et al. (2023) definitions.

Figure 6: Overlap in \(\bar{Q}_{unk}\) (average number of unknown questions per caption) across different models on the DOCCI training set for \(T=20\%\). Each model has a unique set of unknown questions, with about 50% of these shared among all three models.

 

et al., 2024) dataset. We evaluate the models using an automatic NLI model and human annotators (Section 3). In all experiments, we split the DOCCI test set into 1,000 samples for evaluation, while 4,000 samples are used for the reported test set.

## 5 Results

This section presents our experiments, highlighting the impact of KnowAda over competing baselines.

### KnowAda Achieves Better Descriptiveness-Hallucination Trade-off

Figure 3 illustrates that fine-tuning on KnowAda captions consistently provides the best balance between high descriptiveness and low hallucination rates compared to competing baselines. While ICC and DFN offer slight improvements in precision, they do not facilitate further gains due to a lack of control over the trade-off. Although trimming and rephrasing captions allow for some control over precision and recall, they yield inferior results compared to KnowAda across all thresholds.

### KnowAda Is Consistent Across Multiple Models and Datasets

Table 1 shows that fine-tuning on KnowAda captions consistently reduces the hallucination rate while maintaining high descriptiveness across different trained models. This superior performance is evident for both human-annotated and synthetically generated dense captions, as confirmed by our automatic and human evaluation pipelines.

Specifically, KnowAda significantly reduces the contradiction rate in terms of both precision and recall. However, while it improves precision in descriptiveness, it decreases recall. The results in Table 1 are obtained using a stringent threshold of \(T=20\%\), prioritizing hallucination reduction. KnowAda allows increased recall by enabling the selection of a less stringent threshold, facilitating a trade-off between precision and recall, as illustrated in Figure 3. Future work may explore how to maintain KnowAda's improvement in hallucinations while ensuring high recall in descriptiveness.

### Textual Entailment is Highly Correlated to Human Annotated Visual Entailment

We computed the Phi correlation coefficient between the majority agreement labels (ground-truth human annotations) and the labels generated by the NLI model for each proposition. The results yielded a Phi coefficient of \(\phi=0.73\) for the DOCCI original captions and \(\phi=0.67\) for the synthetic captions. These strong positive correlations indicate a significant relationship between the human annotations and our proposed automatic evaluation. It suggests that our text-only NLI model effectively aligns with human judgments of visual entailment, demonstrating its reliability in distinguishing between contradictory and entailed propositions based solely on dense captions.

Moreover, the correlation difference between original and synthetic captions shows that DOCCI human-authored captions are generally more detailed and reliable compared to the synthetic captions, enhancing the effectiveness of the automatic entailment computation using them.

Note that the results from our human annotation process showed an average majority agreement of 82.6% for the "Contradicted" labels and 89.7% for the "Entailed" labels, indicating a high level of consensus among annotators.

### Necessity of Removing Unknown Information

We perform an ablation study to assess the importance of removing VLM-unknown information by following the KnowAda procedure for a fixed \(T=20\%\), but instead of removing information linked to unknown questions, we randomly remove information, a method referred to as _KnowAda Random_ in Table 3. As expected, this results in worse performance across all metrics, demonstrating that unknown information is indeed a problematic factor in captions that impacts the hallucinations.

\begin{table}
\begin{tabular}{c|c c|c c|c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Contradiction \(\downarrow\)**} & \multicolumn{2}{c}{**Descriptiveness \(\uparrow\)**} & \multicolumn{1}{l}{**\# Words**} \\ \cline{2-6}  & Precision & Recall & Precision & Recall & \\ \hline KnowAda Random & 34.7 & 17.2 & 57.9 & 23.9 & 55 \\ _KnowAda_ & **33.7** & **16.7** & **58.9** & **25.8** & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: **Design choices ablations.We ablate the effect of removing unknown information versus removing random information in the KnowAda pipeline. Removing unknown information improves performance across all metrics.** 

Conclusions

This work focuses on fine-tuning small-to-medium-scale vision-language models by aligning dense captions with the models' existing knowledge and visual understanding. It aims to address the challenge of balancing rich descriptiveness with factual correctness in multimodal models, especially when models have limited capacity to process complex visual details in dense captions. By probing models with visual questions and adapting captions to exclude unknown information, we reduce hallucinations while maintaining high descriptive accuracy. Our results, supported by both human and automatic evaluations, suggest that this strategy can lower hallucinations compared to training on original captions or competing data curation baselines.

Furthermore, we introduce a proposition-based evaluation framework that provides fine-grained analysis of generated captions, offering deeper insights into the balance between descriptiveness and fractuality. We believe that our findings contribute to improving fine-tuning methods in VLMs from a data-centric approach, particularly in resource-constrained environments where balancing descriptiveness and accuracy is important for real-world applications. We hope that this work will encourage further research into addressing the challenges of dense captions in VLMs.

## 7 Related Work

Our research is connected to the field of dense image captioning, particularly in addressing unknown information and enhancing fine-tuning datasets. Below, we provide an overview of previous works in each of these areas.

### Dense Image Captioning

Traditional image captioning datasets like COCO Chen et al. (2015) often feature brief captions with limited visual detail. Recently, interest has grown in creating longer and more complex captions. For example, Onoe et al. 2024 and Garg et al. (2024) developed a dataset of 15K human-annotated, richly descriptive captions. Shabtay et al. (2025) used captions of figures of arXiv papers. Additionally, Chen et al. (2023a) and Singla et al. (2024) employed GPT-4 and Gemini to generate detailed captions. We show that these datasets are more effective for fine-tuning when adapted to the specific model being trained.

### Training On Unknown Information

Several works examine the relationship between unknown information and downstream model performance. Gekhman et al. (2024) show that finetuning LLMs on low-confidence examples encourages hallucinations, and suggest that fine-tuning should not introduce new knowledge, but only teach the model to make use of existing knowlegde. Zhang et al. (2024b) suggest optimizing for higher-confidence responses via Direct Preference Optimization (DPO), where the confidence of each response is estimated via self-evaluation. Piche et al. (2024) train a utility function that encourages predicting only high-certainty responses. The function is trained over synthetic data, which is collected iteratively, and is composed of fused high-confidence predicted facts. Additionally, Xu et al. (2024) introduce a refusal mechanism which encourages the model to reject questions that do not align with its existing knowledge. We demonstrate that training a model on questions it is fundamentally lacking the ability to answer, rather than solely on factual knowledge it lacks--particularly in the multimodal domain--results in similar performance degradation. Moreover, our method only rejects the unknown parts of a data sample, while maintaining the information that is useful for training, thus avoiding throwing out useful training samples which only include some unknown parts.

### Data Curation in Fine-Tuning

Recent research has focused on refining the fine-tuning process for pretrained models, especially through instruction tuning. Zhou et al. (2024b) demonstrated that fine-tuning on a small, high-quality instruction dataset can yield superior results. Li et al. (2023b) explored training a language model on a subset of instruction data, using loss discrepancies to inform filtering strategies. However, this approach doesn't extend to image captioning, where no instructions are available. Lin et al. (2024) proposed reducing noisy token influence by assigning lower weights, while Chen et al. (2024b) trained a network to filter out low-loss instruction samples. In contrast, our approach adapts specific parts of captions misaligned with the model's capabilities, rather than filtering or reweighing the entire caption. Our strategy leverages visual knowledge and emphasizes sequences and conceptual coherence, moving beyond token-level adjustments 



## 8 Limitations

While KnowAda demonstrates notable effectiveness, several limitations must be considered.

Firstly, KnowAda is model-dependent, probing the knowledge of each model in isolation. This means that the data must be tailored for each model independently, leading to increased computational overhead.

Secondly, while we show that KnowAda achieves better performance in balancing the descriptiveness-hallucination trade-off, it does not fully resolve this issue. Reducing contradictions to the ground truth often decreases descriptiveness recall (but not precision).

Lastly, our analysis focused specifically on dense caption generation, which is a limited task. Expanding KnowAda to other tasks, such as visual question answering (VQA), could represent an interesting avenue for future research.

## Ethics Statement

This work focuses on measuring and mitigating hallucinations in visual-language models (VLMs). As such, it is expected to increase the reliability of VLMs and the ability to measure their performance, which is important when using them in real-world systems. This is expected to have a positive impact on the use of VLMs in society. However, we recognize that the foundation models used in the KnowAda construction and evaluation pipeline could propagate biases. We anticipate further research into such biases before relying on our work beyond the research environment. The human annotation study performed in this work received the required IRB approval from our institution's ethics committee.

## Acknowledgments

This work was partially supported by Google and the TAU Center for Artificial Intelligence and Data Science. The authors thank Morris Alper and Nimrod Shabtay for their valuable feedback and assistance.

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Appendix

In this appendix, we describe the implementation details in Appendix A.1, qualitative examples in Appendix A.2, additional human annotations details in Appendix A.3 and additional experiments in Appendix A.4.

### Implementation Details

KnowAda Implementation DetailsFor question generation, we use the gemini-1.5-flash-001 model, following the prompt in Figure 14. To generate answers, we set the temperature to 0.4 for all models, sampling 10 answers per question. These answers are evaluated using the gemini-1.5-flash-001 model, with prompts provided in Figure 15. For rewriting image descriptions, we employ gemini-1.5-pro, utilizing manually curated few-shot examples as illustrated in Figure 16. In all experiments, we use greedy sampling when generating outputs with Gemini.

Proposition Entailment Evaluation ImplementationDetailsWe use gemini-1.5-flash-001 both for the proposition extraction, as well as the textual entailment task. We constrain the output to be in JSON format, following the proposition extraction prompt shown in Figure 17 and textual entailment prompt in Figure 18.

Training DetailsWe provide the training hyperparameters in Table 4, all other hyperparameters are set to default. LLaVA and PaliGemma were trained using LLaMA-Factory framework Zheng et al. (2024) and TinyLLAVA was trained using TinyLLaVA-Factory framework Jia et al. (2024). We note that this work fully complies with the licenses of all used scientific artifacts (e.g. DOCCI, LLaVA, PaliGemma, etc.). All use of scientific artifacts is consistent with their intended use. All models were train on a single NVIDIA A6000 GPU.

### Qualitative Examples

In Figure 11, we show example of our evaluation pipeline, including the original description, the generated description and the labels produce by the NLI model.

Figure 12 and Figure13 shows outputs generated by LLaVAs-1.5-7B, trained on KnowAda-adapted captions using various thresholds. As the threshold \(T\) decreases, the model's tendency to hallucinate decreases. However, this comes at the cost of reduced detail coverage. The threshold \(T\) allows for fine-grained control over this trade-off between hallucination and detail retention.

### Additional Annotation Details

To recruit high-quality annotators, we required them to have a HIT rate greater than 97% and at least 5,000 approved HITs, without imposing any geographical constraints. Additionally, we designed a qualification test consisting of 10 questions of varying difficulty, which we manually evaluated. A total of 14 annotators who passed the test with fewer than 2 mistakes were selected to perform the annotations for all experiments.

We present an example from the Amazon Mechanical Turk user interface, as shown to the annotators in Figure 10. Alongside the generated task guidelines, we provided supplementary slides with a detailed explanation of the visual entailment task, including 10 manually annotated examples that feature different labels and varying levels of difficulty for the annotators to review.

Annotators were paid 0.10$ per annotation, with an average hourly wage of 10$. The annotation process was approved by the institution's ethics committee.

Figure 8: **Distribution of number of questions per caption** that are generated by the LLM based on the ground truth image description during the first stage of KnowAda.

Figure 7: Distribution of number of unknown questions per caption for each ratio threshold of correct and incorrect answers from the vlm.

 



### Additional Experiments

**Larger-Scale Dense Caption Dataset** We fine-tune PaliGemma on a dataset that is 10 times larger, sampling 100,000 image-caption pairs from the PixelProse (Singla et al., 2024) dataset. Of these, 95,000 pairs are used for training and 5,000 for testing. We follow the same configurations as in the main paper, except that we train for a single epoch, and report the results in Table 5. As shown, KnowAda significantly reduces the contradiction rate while maintaining high descriptiveness, demonstrating its effectiveness in generalizing to larger-scale datasets.

**Relative Location of Contradicted Propositions.** In Figure 9, we illustrate the distributions of the relative locations of propositions categorized as contradicted with respect to the generated caption. Consistent with prior research (McKenna et al., 2023) We observe that errors tend to increase with the distance from the beginning of the text: as more text is written, the rate of contradictions increases.

**DNLI Evaluation with a Smaller Model** To justify the use of Gemini in the question difficulty evaluation stage, we compared its performance against Gemma-2B by using both models as judges on 1,000 question-answer pairs from the DOCCI test set. The results showed a 75% agreement between the two models. To further analyze discrepancies, we manually inspected 20 random samples where the judgments differed, finding that in 18 out of 20 cases, Gemini provided the accurate assessment. These findings highlight a trade-off between model size and judgment accuracy. While Gemma-2B is a more lightweight alternative, we prioritized the superior accuracy and consistency offered by Gemini for our evaluations.

**Additional KnowAda Statistics** In Figure 8, we show the distribution of the number of questions generated per caption for the DOCCI training set. The data indicates that the distribution is centered around approximately 13 visual questions per caption.

In Figure 7, we demonstrate how the number of unknown questions changes when adjusting the threshold \(T\). As T increases, more questions are classified as unknown, shifting the distribution.

\begin{table}
\begin{tabular}{l c c c c c|c} \hline \hline
**Model** & **Epochs** & **BS** & **LoRA Rank** & **LR** & **Checkpoint** \\ \hline LLaVA-1.5-7B & 3 & 8 & 64 & 0.0001 & llava-hf/llava-1.5-7b-hf \\ TinyLLLaVA & 3 & 8 & 64 & 0.0001 & tinyllava/TinyLAVA-Germna-SigLP-2.4B \\ PaliGemma & 15 & 8 & 128 & 0.0001 & google/paligemma-3b-mix-224 \\ \hline \end{tabular}
\end{table}
Table 4: Training hyperparameters for the models. BS and LR refers to batch size and learning rate.

\begin{table}
\begin{tabular}{l l|r r|r r|r} \hline \hline  & & \multicolumn{3}{c|}{**Contradiction \(\downarrow\)**} & \multicolumn{1}{c|}{**Descriptiveness \(\uparrow\)**} & \\ \cline{3-6}
**Model** & **FT Captions** & **Precision** & **Recall** & **Precision** & **Recall** & **\# Words** \\ \hline PaliGEMma & PixelPose & 44.1 & 40.2 & 43.4 & **38.4** & 70.2 \\ PaliGemma & PixelProse KA & **35** & **25.4** & **54.2** & 38.2 & 47.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: **Dense captioning results** using the automatic version of DNLI on the PixelProse (Singla et al., 2024) test set, when fine-tuning on original captions and KnowAda-adapted captions (denoted as KA) with a 20% threshold.





**Please review the slides for the task guidelines before proceeding: Guidelines Slides is the following proposition Entailed/ContradictedNeutral in relation to the image?**

## Appendix A There are two targets in the image.

### Detailed Instructions

Instructions for determining if the image supports the proposition:

1. Carefully examine the image provided.
2. Next, analyze the proposition above.
3. Determine if the proposition is entalled, contradacted, or neutral based on the image.

Figure 10: **Example of annotation interface in Amazon Mechanical Turk**

Figure 9: **Distribution of contradicted propositions’ relative location in generated captions across different models, tested on the DOCCI test set and measured using our proposition evaluation framework. Contradictions increase progressively as the captions become longer.



Figure 11: **Qualitative Example of Our Proposition-Based Evaluation Metric**. Given a generated description by a VLM (middle), we decompose it into atomic propositions (top, center part). Then, we classify each proposition as either ’Entailed’, ’Contradicted’ or ’Neutral’ (top, right part), conditioned on the ground-truth image description (bottom). Finally, we calculate the consistency and contradiction based on the number of entailed and contradicted propositions.

 



### Original:

A medium-close-up view of a dark brown wooden dresser that is sitting on a light brown tile floor. The dresser has a gloss rose sitting on top of it, and the vase is filled with water. The vase has a clear bose and a clear neck, and it is filled halfway with water, and on the neck of the vine there is a small crystal bowl. The light bulb is sitting inside of a white glass lamp shade. The lamp is sitting at the top of the dresser. On the right side of the lamp, there is an oval mirror that is reflecting the tomp and the dressers. The mirror is being held up by a silver metal pole. The walls surrounding the dressing room are made up of wooden planks that run vertically.

#### T=90%:

A medium-close-up view of a wooden dresser that is placed on a white tidler door. The dresser is mode up of dark brown wooden panels that run horizontally, and along the panels there are small drawers that have gold handles. The top portion of the dresser has a small lamp that is mode of glass, and the glass is being lit up by a tight bulb. On the left side of the lamp, there is a small vase that is being held up by two thin sticks. On top of the vase, there are two small glasses that are being held together by a thin metal rod. On both sides of the mirror, there's a wooden wall that is covered in wooden planks that run vertically.

#### T=80%:

A view of a wooden desk with a lamp on it. The desk is dark brown and has a flower on the left and one on the right. The drower in the middle has a small block hole in it. A small black box is on the floor in front of the desk. The lamp is on top of the drowser. It has a white shade on it and a gloss base. The base has a reflection of the lamp on the inside of it. On the left of the frame is a white outlet with a cord plugged into it.

#### T=60%:

A view of a wooden desk with a lamp on it. The desk has drawers on it and a small shelf under it. A lamp is on the desk. The lamp has a white shade on it, and it has a gloss base. The base has a clear gloss base with a small round bose on it that has a small white light bulb in it. There is a small gloss rose on the left of the lamp. The vase has a round bose and a clear bose. A mirror is on a well behind the desks. The mirror has a frame around it.

#### T=40%:

A view of a wooden desk with a lamp on it. The desk has drawers on it and a small shelf under it. A mirror is on the wall above the desk. The mirror has a frame around it. Light is shining on the desks and the wall. The floor is tilted

#### T=20%:

A view of a brown wooden desk with a lamp on it. The desk has drawers on it and a small shelf under it. A mirror is on the wall above the desk. The mirror has a frame around it. It is hanging on the wood wall. The wall is mode of wood planks. The floor is tiled.

Figure 12: **Qualitative Example of KnowAda’s Threshold \(T\) effect**. We apply KnowAda’s rephrasing to the original caption (bottom) using different T values.



Original:

A medium-close-up view of \(\alpha\) statue that is placed on \(\alpha\) gray cement platform. The statue is of \(\alpha\) woman who is sitting down and is facing right. The woman is wearing \(\alpha\) dress that is made up of a golden material, and along the dress there are golden markings. The dress is being held up by \(\alpha\) golden belt but that is wrapped around the woman's waist. The left arm of the women is placed along the right side of the statue, and the right arm is placed behind the woman. The right hand of the arm is holding onto the left arm. The head of the sculpture is facing slightly to the right, and it is a bust of \(\alpha\) man who has short hair and \(\alpha\) beard. The mon is sweeping, and his right hand is placed against his chest. The bust is being lit up by the sun. Behind the statue there is \(\alpha\) gray building that is covered in shade.

T=90%:

A medium-close-up view of \(\alpha\) statue that is placed on \(\alpha\) grey cement platform. The statue is of a woman who is sitting on \(\alpha\) chair, and she is facing forward. The womon is weering \(\alpha\) dress that is mode up of gold and silver. The dress is made of \(\alpha\) long skirt, and along the skirt there are two gold lines that run vertically. The skirt is made out of a gold and white dress, end it is made to look like \(\alpha\) bell. The top portion of the dress is white, and on top of the white portion there is \(\alpha\) gold one. The head of the woman is mode like \(\alpha\) bust, and the neck of the bus is made from gold. The arms of the statue are thin, and they are made of gold. On the left arm of the lady, there is something that is being held. The face of the person is mode with \(\alpha\) lot of detail, and surrounding the face there is gold. To the right of the chin; there are stoutes of two people who ore sitting on their knees. The person on the left is \(\alpha\) man, and he is facing to the right. The mon is wetting \(\alpha\) white dress that hos gold trimming. The other person is \(\alpha\) woman, and her face is mode in \(\alpha\) similar way to the womns. The legs of the people are thin and mode of white marble. The window of the building is made into \(\alpha\) rectangle, and running along the window there are thin lines that are mode vertically and horizontally

T=80%:

A medium-close-up view of \(\alpha\) statue that is placed on \(\alpha\) gray cement platform. The statue is of \(\alpha\) woman who is sitting on \(\alpha\) pedestal. The woman is facing forward, and she is wearing \(\alpha\) dress that is mode up of white fabric. The dress is being held up by \(\alpha\) gold bett that is wrapped around the woman's waist. The head of the woman is being supported by \(\alpha\) golden crown that is being worn by the woman. The crown is being placed on the woman by \(\alpha\) woman' figure that is sitting behind the woman and is facing the right side. The figure is weary, and it is being lit up by the sun. The right arm of the figure is being wrapped around \(\alpha\) woman, and the left arm is being used to hold up the crown. The face of the statue is being covered by \(\alpha\) veil. The cement pedestals are being held together by \(\alpha\) metal pole that runs vertically.

T=60%:

A view of \(\alpha\) statue of \(\alpha\) woman and \(\alpha\) man. The woman is sitting on \(\alpha\) platform, and she is facing forward. She has \(\alpha\) crown on her head. She is wearing \(\alpha\) dress. The mon is sitting in front of her, and he is facing her. He has no clothes on. He is holding his right arm up. The statue is on \(\alpha\) gray stone platform. The sun is shining on it. A building is behind the statue

T=40%:

A view of \(\alpha\) statue of \(\alpha\) woman sitting on \(\alpha\) pedestal. The woman is facing forward and has her right arm around the boy. The boy is facing away from the woman and has his right arm wrapped around her. The statue is placed on \(\alpha\) gray cement pedestols. The pedestal is placed in front of \(\alpha\) building.

T=20%:

A view of \(\alpha\) statue of \(\alpha\) woman and \(\alpha\) man. The woman is sitting on \(\alpha\) pedestal. The mon is sitting in front of her. The statue is on \(\alpha\) gray stone pedestrians. The pedestal is on top of \(\alpha\) gray cement base. The boise is on the ground. The ground is covered in shadows.

Figure 13: **Additional Qualitative Example of KnowAda’s Threshold \(T\) effect**



System PROMET:

Task Overview:

You are on AI visual assistant observing a single image. Accomparing this image is a paragraph that describes it.

Guidelines:

Formulate Relevant Questions Your primary task is to generate multiple questions as if you ore actively viewing the image.

Adopt on AI Tone: Frame your questions in a tone consistent with that of a visual AI assistant, focusing on analyzing the visual content.

Focus on Visual Details: Your questions should pertain to specific aspects of the image, such as

Types of objects present, Consuring the objects, actions performed by the objects, Locations and relative positions of objects, Background knowledge related to the objects

Ensure Definitive Answers: Only ask questions that can be comfortably answered based on the visible content of the image. Each question should meet the following criteria:

It can be confidently observed from what is seen in the image, so described in the caption.

Is clear that the answer can be determined or absent if the information is not present.

Avoid Uncertion Details: Do not include questions that cannot be answered confidently. Focus on complex, relevant negatives that delve into the visual elements and contextual significance within the image.

Response Structure:

Please structure your response as follow:

Quantization:

Cvour first questions:

*Your second questions:

*Your third question:

_--_

EPEN/SHOTEDMENT:

An indoor upwards engaged view of a wooden beam light future with six incondescent light bulbs honoring down from the frugged edges of a rope wrapped around the wooden beam. A method is attached to the left end right charged ends of the wooden beam and one complained and continues upwards toward the ceiling, where it is plugged into white selectircuit outlet, A white x-c, vertical style mounted into the upper wall in the background with an end while free sprinkler for its right. A white room door with a block metal rating is visible below and behind the light bulbs, with a block menu board and white letters that read 35800 K/S2670K/S2670K to Its left. A doorwork with grade door turns visible to the lower right, where the glow of a yellow light is visible.

1. Question: What is the perspective of the imag?

2. Question: What material is the light future made from

3. Question: How many light bulbs are hanging from thefuture?

4. Question: What is the condition of the rope holding the light future?

5. Question: What material is the chain attocted to the light future made from?

6. Question: What is the color of the cabin attached to the light future?

7. Question: What is the condition of the ends of the woods been?

8. Question: What is mounted on the upper wall in the background of the image?

9. Question: What color is the size further to the AC vort?

10. Question: What type of door is visible before the light future?

11. Question: What color is the ruling out here to show proof?

12. Question: What is the material of the refining need to the bern floor?

13. Question: What is located to the left of the bump door?

14. Question: What is color of the board?

15. Question: What color was the letters on the menu board?

16. Question: What is written on the main board?

17. Question: What is visible through the doorwork in the laser right corner of the image?

18. Question: What color is the light glowing from the doorwork?

A top-down, close-up view of a brown, farey aliqger sitting on too of o leaf but is slightly wide. On the far left side of the frery slioper; there is a discordord flower that used to be blue, while the cone underneath it is to dark bias. On the right bottom portion of the frery slioper; there is a floaded flower as well.

1. Question: Which is the overall genegebies of the scene?

2. Question: What is the distance and zoom level of the viewpoint?

3. Question: When type of insects is seen on top of the leaff?

4. Question: Where is the arm color of the insert in the scene?

5. Question: How will is the left of the insect is sitting on compared to its body?

6. Question: Is there flower to the left the inset?

7. Question: What is the condition of the flower to the left bottom of the insect?

8. Question: This is the color of the fish button of the insect?

9. Question: What is the color of the flower to the right bottom of the insect?

10. Question: What is the color of the flower to this industrial theme in insect?

11. Question: Is there a flower to the right bottom of the insect?

12. Question: What is the condition of the flower to the left of the insect?

A distorted and blurry outdoor images of a red colored octagon shaped sign in front of various leaves and trees with the clouded gray sky visible at the top of the vine site? Stop two? Or inkts, which outline clored border around the outside of the tip.

1. Question: Why is the correct identity of the image?

2. Question: It is the image token idors or adjacent?

3. Question: how is the object shape of the ship in the image?

4. Question: Wheel color is the size of the finger?

5. Question: There is other test time step?

6. Question: What color is the root on the sign?

7. Question: If there is no longer around the ship?

8. Question: Where is better or more than we might, what color is the border?

9. Question: What is visible in the background behind the sign?

10. Question: What is the color of the sky visible in the image? 

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* [17] T. Gershman, "Learning from convolutional neural networks with applications to image recognition," in _IEEE International Conference on Acoustics and Speech (ICASSP)_, pp. 1-10, 2016.

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