FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model (2024)

Abstract: Most existing image captioning evaluation metrics focus on assigning a single numerical score to a caption by comparing it with reference captions. However, these methods do not provide an explanation for the assigned score. Moreover, reference captions are expensive to acquire. In this paper, we propose FLEUR1 , an explainable reference-free metric to introduce explainability into image captioning evaluation metrics. By leveraging a large multimodal model, FLEUR can evaluate the caption against the image without the need for reference captions, and provide the explanation for the assigned score. We introduce score smoothing to align as closely as possible with human judgment and to be robust to user-defined grading criteria. FLEUR achieves high correlations with human judgment across various image captioning evaluation benchmarks and reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S within the domain of reference-free evaluation metrics. Our source code and results are publicly available at: https://github.com/ Yebin46/FLEUR.

heading: 1 Introduction
text: Evaluating image captions is essential as it provides a significant indicator of the model’s ability to understand visual and language information effectively (Mokady et al., 2021; Li et al., 2023). However, there are two primary challenges with existing image captioning evaluation metrics. Existing methods 1) require reference captions to evaluate candidate captions2 and 2) lack explainability.
*Equal contribution. Correspondence to: Myungjoo Kang 1 We choose a word in French that means ‘flower’, in line with other French-named evaluation metrics. 2 A reference caption refers to the human-annotated caption for an image. A candidate caption refers to the caption that is to be evaluated.
First, traditional image captioning evaluation metrics (Papineni et al., 2002; Lin, 2004; Banerjee and Lavie, 2005) have the drawback of requiring reference captions. These metrics assign scores to candidate captions by comparing them to reference captions. However, in practice, obtaining reference captions is challenging because it requires human annotators to create reference captions. Furthermore, evaluating captions only based on text without direct image comparison cannot yield accurate scores. Therefore, new methods (Hessel et al., 2021; Sarto et al., 2023; Hu et al., 2023) have
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emerged that evaluate captions without the need for reference captions by incorporating images.
Second, existing evaluation metrics still lack explainability. Throughout this paper, we clarify the meaning of an explainable metric. As defined in Leiter et al. (2022), we embrace the broad concept of explainability for metrics. The explainability contains the ability to provide an explanation for the score obtained from the metric. Existing metrics cannot provide intuitive explanations in sentence form. This makes it difficult to discern whether the score is accurate or not. Hence, we categorize metrics incapable of providing descriptive explanations as non-explainable metrics (see the top of Figure 1).
To overcome these two limitations, we propose a reference-Free expLainable EvalUation metRic (FLEUR) for image captioning. FLEUR can evaluate captions even in the absence of reference captions and provide explanations for the scores by using a large multimodal model (LMM). We introduce score smoothing to calibrate the scores from the LMM more finely and make FLEUR robust to prompts. Additionally, we propose a prompt including grading criteria for caption evaluation to align the scores more closely with human judgment. It is noteworthy that FLEUR is the only caption evaluation metric both explainable and reference-free.
FLEUR achieves state-of-the-art results across multiple benchmark datasets among the referencefree evaluation metrics, calculated through correlations with human judgment. Furthermore, we demonstrate FLEUR’s explainability by comparing its explanations with those of CLAIR (Chan et al., 2023), a reference-based and explainable evaluation metric. We hypothesize that directly viewing the image enables a more accurate and comprehensive evaluation of a candidate caption as shown at the bottom of Figure 1.
Our contributions are as follows:
• We propose FLEUR, an explainable referencefree image captioning evaluation metric. FLEUR achieves the highest correlations with human judgment across various benchmark datasets.
• To the best of our knowledge, our work is a pioneering work of using an LMM to evaluate image captions. We improve the rating performance of an LMM by introducing score smoothing and grading criteria.
• Through a comparison with the referencebased metric CLAIR, we show that FLEUR generates better explanations because it can consider images.

heading: 2 Related Works

heading: 2.1 Image Captioning Evaluation Metrics
text: Reference-only metrics compare only the reference captions with the candidate caption (Vedantam et al., 2015; Anderson et al., 2016; Zhang et al., 2020). However, reference captions cannot fully encapsulate the image, and natural language inherently contains ambiguity (Jiang et al., 2019). To address this challenge, reference+image metrics have been proposed (Jiang et al., 2019; Lee et al., 2020; Inan et al., 2021; Wada et al., 2024). These metrics consider the image in conjunction with reference captions. Nonetheless, the issue persists that acquiring a set of reference captions for an image is costly and challenging. Therefore, reference-free metrics have been proposed to evaluate candidate captions even in the absence of reference captions (Hessel et al., 2021; Sarto et al., 2023; Hu et al., 2023). Reference-free metrics address the difficulty of requiring reference captions by evaluating candidate captions against images. However, there is no explainable metric among reference-free metrics. Bringing the advantage of a reference-free metric, FLEUR addresses this issue by using an LMM to provide explanations of the scores.

heading: 2.2 LLM-based Evaluation Metrics
text: Metrics for NLG evaluation There is a growing interest in leveraging the powerful performance of a large language model (LLM) to evaluate natural language generation (NLG) quality (Fu et al., 2023; Chiang and Lee, 2023). G-Eval (Liu et al., 2023c) assesses the performance of NLG by using an LLM in a form-filling paradigm. Liu et al. (2023c) propose the method that uses the probabilities of output tokens from the LLM, and takes the weighted summation as the final score. G-Eval has to estimate probabilities by sampling 20 times because they use proprietary LLMs like GPT-4 (Achiam et al., 2023). In contrast, FLEUR employs an open-source LMM to utilize actual probabilities outputted by the model.
Metrics for caption evaluation In the field of image caption evaluation, CLAIR (Chan et al., 2023) first employs text-only LLMs to compare reference caption sets with candidate captions.
CLAIR can provide explanations for the scores, making it an explainable metric. However, CLAIR evaluates candidate captions without considering images at all. If the candidate caption contains information that cannot be inferred from the reference caption set, we cannot expect an accurate score (see the bottom of Figure 1). On the other hand, FLEUR considers the image and therefore provides better evaluation scores and more appropriate explanations.

heading: 3 Method
text: The overall framework of FLEUR is depicted in Figure 2. FLEUR derives a raw score based on the prompt that accurately reflects grading criteria (orange speech bubble on the left of Figure 2), and makes the score continuous by using the probabilities computed by an LMM for score smoothing (blue dashed line box in Figure 2). Words marked in italics will be explained in detail in the following sections. In this paper, we opt for LLaVA (Liu et al., 2023a) as our LMM to introduce score smoothing. Please refer to Appendix A for a specification of LLaVA that we used.

heading: 3.1 Prompt for Caption Evaluation
text: Our prompt consists of two parts: 1) base instruction, and 2) grading criteria. The base instruction contains essential information for performing caption evaluation, while the grading criteria provide guidelines on how to score during caption evaluation. Our whole prompt is as follows:
1)    Your task is to evaluate and rate the caption on a scale of 0.0 to 1.0 based on the given Grading Criteria. (Print Real Number Score ONLY)
2)    Grading Criteria: 0.0: The caption does not describe the image at all. 1.0: The caption accurately and clearly describes the image.
Caption: {caption}
Score(Choose a rating from 0.0 to 1.0):
Base instruction Similar to previous works (Chan et al., 2023; Liu et al., 2023c), the base instruction explains what task LLaVA should perform. This may include specifications such as the range of scores or requirements for the output format. We request output in the form of a real number between 0 and 1. Additionally, by including a directive to score based on grading criteria, we guide LLaVA to produce convincing scores.
Grading criteria The grading criteria provide LLaVA with more detailed guidelines on how to evaluate captions based on scores. Intuitively, this approach distinguishes itself from non-explainable metrics by offering the advantage of aligning scores more closely with user intentions. We base the grading criteria for FLEUR on the actual criteria used by human evaluators. Refer to Section 5.1 for detailed grading criteria selection.

heading: 3.2 Score Smoothing
text: We propose a process called score smoothing to calibrate the raw score, which is the output of LLaVA. Raw score can be seen as a score before score smoothing. Score smoothing utilizes the probabilities of tokens corresponding to digits from 0 to 9. To illustrate, in Figure 2, given an image and the prompt as input, LLaVA outputs “0.85”. Let’s consider the process by which LLaVA generates the output. After LLaVA generates tokens up to the decimal point (i.e., “0.”), it computes the probabilities of all tokens and chooses the next token “8” since token “8” has max probability.3 In this computation, we can obtain the probability pk of the token corresponding to each digit k (0 ≤ k ≤ 9) for score smoothing. Similarly, in the process of selecting the token “5” for the second decimal place, we can obtain the probability qℓ of the token corresponding to each digit ℓ (0 ≤ ℓ ≤ 9). Then, the FLEUR score of that image-caption pair is computed as follows:
FLEUR
= 0.1× (0× p0 + 1× p1 + · · ·+ 9× p9) + 0.01× (0× q0 + 1× q1 + · · ·+ 9× q9).
Formally, while generating the raw score, LLaVA computes the probability p(i, j) of each token corresponding to the digit i (0 ≤ i ≤ 9) appearing at the j-th decimal place ( j = 1, 2), and we define FLEUR as follows:
FLEUR = 2∑
j=1
10−j 9∑
i=0
i× p(i, j).
Please refer to Appendix B for implementation details of FLEUR.
We calibrate the raw score because LLaVA tends to have some scores dominate the distribution of raw scores. This tendency leads to an increase in ties among the scores. In the case of many ties, it is not suitable as an evaluation metric as it cannot discern subtle differences between candidate captions. Even when the raw scores of two image-caption pairs are the same, LLaVA may have different probabilities for those tokens. Consequently, through the application of score smoothing, the scores attain greater granularity, mitigating instances of ties. In Section 5.2, we show the positive impact on score smoothing through experiments.
3 When LLaVA generates the next token, we use greedy decoding to make FLEUR a deterministic metric.

heading: 3.3 RefFLEUR
text: In cases where reference captions are provided, FLEUR can be extended to consider the reference caption set when scoring, called RefFLEUR. In this case, we provide reference captions and a candidate caption in the prompt. The prompt that overlaps with that of FLEUR is omitted with ‘...’, and the modified parts are indicated with underline. The prompt for RefFLEUR is as follows:
Your task is to evaluate and rate the candidate caption on a scale of 0.0 to 1.0 based on the given Grading Criteria. (Print Real Number Score ONLY) . . . 1.0: The caption accurately and clearly describes the image.
Reference Captions: {reference caption set}
Candidate Caption: {candidate caption} . . .

heading: 3.4 Prompt for Explanation
text: The explanation for the FLEUR score can be obtained by inputting an additional prompt into LLaVA. As shown on the right of Figure 2, we use the prompt “Why? Tell me the reason.” Unlike previous studies (Chan et al., 2023; Chiang and Lee, 2023), we separate the prompts for requesting the score and the explanation to reduce inference time. Examples of the explanations obtained through this prompt are in Figure 3 and Appendix D.

heading: 4 Experiments

heading: 4.1 Correlations with Human Judgment
text: To assess the quantitative performance of FLEUR, we measure its correlations with human judgment and compare them with other caption evaluation metrics. Following previous studies (Hessel et al., 2021; Hu et al., 2023), we evaluate FLEUR on Flickr8k (Hodosh et al., 2013), COMPOSITE (Aditya et al., 2015), and Pascal-50S (Vedantam et al., 2015) datasets. Please refer to Appendix C for a comprehensive overview of the datasets.
Performance measures of evaluation metrics Similar to the previous work (Sarto et al., 2023), we use Kendall’s tau correlation coefficient τ and accuracy to evaluate the performance of image captioning evaluation metrics. To be more specific, we
use tau-c (τc) for Flickr8k-Expert and COMPOSITE, and tau-b (τb) for Flickr8k-CF. For Pascal-50S, we employ accuracy as the measure, as it assesses whether it aligns with human annotators’ preference among two candidate captions.
Metrics to compare Metrics can be divided into two criteria: whether they are explainable (‘Exp’ in Table 1) and whether they require reference captions (‘Type’ in Table 1). Excluding our metrics and CLAIR, the remaining metrics are nonexplainable because they cannot provide explanations for their scores. Metrics in the first and second block of Table 1 are reference-based metrics. Metrics in the first block only rely on reference captions for evaluation (reference-only), while metrics in the second block evaluate captions along with the image (reference+image). Metrics in the third block are reference-free metrics. Note that FLEUR is the only caption evaluation metric both explainable and reference-free.
4 We only consider the version of CLAIR that uses GPT-3.5. Due to the disparity in the calculation method of Kendall’s correlation coefficient between CLAIR’s official results and other metrics, we obtain the CLAIR results publicly available on https://github.com/ DavidMChan/clair/issues/2 and calculate Kendall’s correlation coefficient directly in the same setting.
5 In the InfoMetIC paper, due to the uncertainty of the Pascal-50S setting, we do not report the score of InfoMetIC. We follow the setting in CLIPScore (https://github. com/jmhessel/clipscore/issues/4).
Results Tables 1 presents a comprehensive summary of the results. There are three important takeaways from the results.
First, FLEUR achieves the highest correlation coefficient among reference-free metrics, excluding evaluation on Flickr8k-Expert. Even when including reference-based metrics, FLEUR attains the second-best correlation. Note that RefFLEUR, a variant of FLEUR, achieves the best performance. This implies that FLEUR can evaluate captions closely to human judgment, even in the absence of reference captions.
Second, FLEUR achieves the highest accuracy among reference-free metrics on Pascal-50S. RefFLEUR also achieves the second-best accuracy among reference-based metrics6 . However, in HC, there is a noticeable difference in accuracy between FLEUR and RefFLEUR. The two candidate captions in HC are both correct human-annotated captions with subtle differences. We speculate that considering reference captions together allows for better detection of these subtle differences, leading to higher accuracy.
Third, FLEUR outperforms the explainable reference-only metric CLAIR. We speculate that this is because FLEUR can evaluate the caption
6 Note that Polos is released after the submission of our manuscript. At the time of submission, RefFLEUR had the best performance.
against the image and generate the more suitable score. A qualitative comparison among explainable metrics strengthens this speculation (see Section 4.2).

heading: 4.2 Comparison with Other Explainable Metric
text: We compare the explanations of CLAIR with FLEUR’s explanations for Flickr8k-Expert. As shown in Figure 3, CLAIR’s explanations often contain inaccuracies. This is particularly evident when candidate captions align somewhat with the reference but include additional information not present in the references. In contrast, FLEUR can evaluate candidate captions based on the image without relying on information from the reference caption.
For example on the left of Figure 3, the candidate caption mentions the color of the surfer’s clothing (black), but the reference caption set only contains information about the color of the surfboard (blue). CLAIR cannot discern the correctness of such information, and this leads to the generation of incorrect explanations. On the other hand, FLEUR, which can consider the image, can identify the color of the surfer’s clothes in the image. Consequently, we speculate that FLEUR provides better evaluation scores and generates more appropriate explanations than CLAIR by directly comparing candidate captions with images.

heading: 4.3 Evaluation of Object Hallucination
text: We check whether FLEUR can effectively detect object hallucination, mentioning objects that are not present in images, in FOIL (Shekhar et al., 2017) dataset. FOIL is created by replacing a noun phrase related to a single object in the MSCOCO (Lin et al., 2014) caption (e.g., changing ‘cat’ to ‘dog’). We measure the accuracy of detecting object hallucination by comparing the scores of the original caption and the perturbed caption.
In Table 2, only the results of the metrics that achieved the highest accuracy on FOIL for each block of Table 1 are presented. FLEUR surpasses the accuracy of the previous state-of-theart reference-based metric, RefPAC-S. Additionally, FLEUR achieves significantly higher accuracy than the state-of-the-art reference-free metric, PAC-S. Note that all reference-based metrics
show a tendency for accuracy to increase with a greater number of reference captions. In contrast, FLEUR achieves overwhelming state-of-theart performance without using any reference. This indicates that FLEUR is highly robust against object hallucination.

heading: 5 Ablation Studies

heading: 5.1 Effect of Grading Criteria
text: To find the most appropriate grading criteria for evaluating captions, we conduct an ablation study based on the grading criteria that human annotators actually use on Flickr8k-Expert. Annotators for Flickr8k-Expert grade candidate captions on a scale of 1 to 4 as integers. The grading criteria referenced by annotators have standards for each score. We normalize integer scores based on our score scale, ranging from 0 to 1, and use them in our grading criteria. The guidelines based on normalized scores are as follows:
0.0: The caption does not describe the image at all. 0.3: The caption describes minor aspects of the image but does not describe the image. 0.7: The caption almost describes the image with minor mistakes. 1.0: The caption accurately and clearly describes the image.
Figure 5a is the result of the ablation study based on the scores included in the grading criteria. ∅ signifies the absence of grading criteria. a represents grading criteria of 0.0 and 1.0, while b and c each denotes grading criteria of 0.3 and 0.7, respec-
tively.7 Experimentally, when scores are included in the grading criteria, LLaVA tends to output those scores more frequently. As the number of grading criteria increases, the raw score tends to have a higher correlation with human judgment. Based on this result, we can draw two observations. Introducing detailed grading criteria helps increase correlation with human judgment, and score smoothing makes the model more robust to prompts than raw scores. We select the grading criteria for 0.0 and 1.0, which show the highest correlation with human judgment, as our main method.
7 For instance, {a, b} represents grading criteria including guidelines for scores of 0.0, 0.3, and 1.0.

heading: 5.2 Effect of Score Smoothing
text: The raw score can be considered as an ablated setting that removes score smoothing in FLEUR. We examine the quantitative impact of score smoothing on the performance of the benchmark datasets. As shown in Table 3, performance is better with score smoothing (FLEUR) by a large margin for all datasets, except for Flickr8k-CF, compared to the case without score smoothing (Raw).

heading: 5.2.1 Impact on Explanations
text: If the scores vary for the same image-caption pair, would the explanations for the scores also differ? Surprisingly, when requesting explanations for a FLEUR score and a raw score, the explanations do indeed differ. Therefore, we assess the qualitative impact by comparing explanations of a FLEUR score and a raw score for the same image-caption pair on a per-sample basis.
As shown in Figure 4, depending on the score, it can even change whether the candidate caption is considered appropriate or inappropriate in the explanation. In the right side of Figure 4, despite the candidate caption inaccurately describing the woman’s attire, the raw score assigns a high score (0.75) which contradicts human judgment. The explanation derived from this raw score says that the candidate caption describes the image well. On the other hand, FLEUR assigns a lower score (0.5893) which aligns with human judgment. The explanation derived from this FLEUR score says that the candidate caption does not effectively describe the image. Based on the examples in Figure 4 and the results in Table 3, it can be inferred that FLEUR leads to more accurate explanations.

heading: 5.2.2 Advantages of Obtaining Probabilities
text: To assess the impact of directly obtaining probabilities using an open-source model, we compare the performance of sampling to estimate probabilities with directly obtaining probabilities on Flickr8kExpert. Similar to G-Eval (Liu et al., 2023c), we let some randomness in LLaVA’s responses (temperature ̸= 0), and N samples are drawn
Model Flickr8k COM Pascal-50S (Accuracy)
EX (τc) CF (τb) (τc) HC HI HM MM Avg
MiniGPT-v2 44.0 23.7 41.5 50.0 95.4 82.5 64.4 73.1 InstructBLIP 45.6 34.0 53.5 52.2 98.8 92.9 67.5 77.9 FLEUR 53.0 38.6 63.5 61.3 99.7 97.6 74.2 83.2
Table 5: Ablation study of LMMs. Note that we use slightly different prompts for each model.
to consider the average response as the final score. Figure 5b illustrates the τc values for different sampling counts N on Flickr8k-Expert. As N increased, the correlation with human judgment also increased. However, even up to N = 20, directly obtaining probabilities and applying score smoothing perform better in terms of correlation compared to sampling. FLEUR, which uses score smoothing, is deterministic and advantageous from an inference time perspective as it only requires sampling once.

heading: 5.3 Effect of LMM
text: Model size We compare the impact of LLaVA model size on performance in Table 4. In all benchmark datasets excluding HM, the 13B model outperforms the 7B model. In HM, the performance difference between the 7B model and the 13B model is indeed minimal (0.1).
Other models We assess FLEUR with opensource models, MiniGPT-v2 (Chen et al., 2023), InstructBLIP (Dai et al., 2023), and LLaVA as candidates for our LMM. These models have good performance in various vision-language tasks. However, MiniGPT-v2 outputs scores in the form of “0 5” and “0 9”, instead of the format we expect (i.e., real number). Similarly, on Flickr8k-Expert, InstructBLIP only outputs one of the three scores {0.5, 0.8, 1.0}, with approximately 99% of the outputs being 0.5. Despite the poor raw scores of these models, the results significantly improved after applying score smoothing (See Table 5). Since LLaVA still achieves the highest correlation with human judgment, we select LLaVA as our LMM. By finding a prompt tailored to each model, our method can be extended to other LMMs. We leave it to future work.

heading: 6 Discussion and Conclusion
text: We propose an explainable reference-free metric, FLEUR, achieving notable correlations with human judgment. Our metric utilizes the latest LMM,
LLaVA, to compare only an image and a candidate caption without reference captions. Therefore, no additional cost and time such as creating reference captions is needed. Directly comparing an image and a candidate caption allows for appropriate scoring and the generation of a reasonable explanation. This explainability not only unveils the reason behind the evaluated score, but also holds the potential to provide insights that can enhance image captioning model performance, or to be utilized as a dataset by employing score-explanation pairs in training other models.
Note that FLEUR is the first work to use an LMM in caption evaluation metrics. It may be possible to find more suitable prompts through prompt engineering (White et al., 2023; Voronov et al., 2024) in order to obtain better scores and explanations. Furthermore, FLEUR can be extended to other LMMs through prompts tailored to the model being used, rather than the prompts we employed. We hope our work can contribute to the explainability of image captioning evaluation metrics.
Limitations
Our approach is straightforward and demonstrates good evaluation performance, but as we evaluate image captions using an off-the-shelf LMM, it inherits the issues associated with the LMM itself.
Necessity of post-processing Despite the request to output only a real number in the prompt, it cannot be guaranteed that the output of the LMM conforms to the request (Chan et al., 2023). Rarely, there are outputs other than scores (e.g., Score:). We remove any additional text and extract only the scores. Fortunately, in our experimental setup, there are no instances where scores are not obtained for all benchmark datasets.
Hallucination There may be concerns about hallucination in FLEUR’s explanations. In reality, FLEUR generates nearly accurate explanations when assigning scores similar to human judgment on benchmark datasets. However, when assigning incorrect scores, FLEUR generates explanations with hallucinations (see Appendix E for our failure cases). Nevertheless, this concern is less likely compared to other non-explainable metrics, and as it is actively researched in the current field of LLMs, addressing the inherent issues in LLMs will naturally contribute to resolving this problem.
Preference for LLM-based output We investigate whether there is a tendency to prefer sentences generated by LLMs, as seen in the previous evaluation metric using LLMs (Liu et al., 2023c). We compare reference captions from Pascal-50S with short descriptions generated by LLaVA, evaluating them using FLEUR. For details on generating short descriptions, please refer to Appendix F. As observed in Liu et al. (2023c), FLEUR assigns an average score 0.1 higher (standard deviation: 0.087) to short descriptions generated by LLaVA compared to reference captions. However, we observe that the captions generated by LLaVA often contain more detailed information than the reference captions. For instance, LLaVA’s generated captions include descriptions of the bird’s color and species, while the reference captions simply mention ‘bird’. However, the opposite case also exists, and due to the difficulty in evaluating this issue, it can be considered a limitation of our study.
Inference Time Due to the use of an LMM, FLEUR requires longer inference times compared to other metrics. The comparison of inference times is provided in Table 6 in Appendix. To measure inference times, we evaluated metrics on a system equipped with a GeForce RTX 3090 and an Intel Xeon Silver 4210R. While SPICE and CLIPScore have inference times of less than 20 ms per sample, FLEUR and RefFLEUR require approximately 0.70 s and 0.76 s per sample, respectively. However, these times remain within acceptable limits, as evaluating the entire Flickr8k-Expert dataset takes about an hour, which falls within the reasonable range for metrics.
Generation Order of Explanation Due to the auto-regressive nature of LMMs, generating the explanation after the score (score-explanation order) can be considered post-hoc rationalization. To address this, we conduct experiments to generate explanations before scores (explanation-score order). As a result, generating in explanation-score order achieves a high human judgment correlation similar to the FLEUR (see Appendix G for details). This demonstrates score smoothing is relatively robust to prompts, and FLEUR that obtained by conducting caption evaluation in the score-explanation order is meaningful. Despite being considered posthoc rationalization, the advantage of our approach lies in enhancing understanding of scores compared to existing metrics that do not provide explanations and facilitating factual verification.

heading: Acknowledgements
text: We thank Jaewoong Choi and Jaemoo Choi for their valuable feedback. We thank all the anonymous reviewers for their constructive comments. This work was supported by NRF grant[RS-2024-00421203], and MSIT/IITP[2021-0-01343-004, Artificial Intelligence Graduate School Program (Seoul National University)].

heading: A Model Details
text: LLaVA (Liu et al., 2023b) is a multimodal large language model that integrates a pretrained CLIP
(Radford et al., 2021) ViT-L visual encoder and a large language model (LLM) using MLP projection. The model was trained using MSCOCO (Lin et al., 2014) bounding box and caption datasets, with instructions generated by the text-only GPT-4 (Achiam et al., 2023). Despite being fully open source, LLaVA demonstrated performance comparable to GPT-3.5 across various downstream tasks.
The version of the model used in our study is ‘liuhaotian/llava-v1.5-13b’. The differences between LLaVA v1.0 (Liu et al., 2023b) and LLaVA v1.5 (Liu et al., 2023a) lie in the types of base models, the amount of data used for training, and the addition of a simple formatting prompt. For the visual encoder, LLaVA v1.0 utilizes CLIP ViT-L/14, while LLaVA v1.5 uses CLIP ViT-L/14@336px. As for the LLM, LLaVA v1.0 employs LLaMA (Touvron et al., 2023), and LLaVA v1.5 uses Vicuna (Chiang et al., 2023).
B Implementation Details
The following is an example of FLEUR calculation for the sample in Figure 2:
0.80664
≈ 0.1× ( 0× 0.003021240234375
+ 1× 0.00128936767578125 + 2× 0.0018758773803710938 + 3× 0.00353240966796875 + 4× 0.00827789306640625 + 5× 0.03350830078125 + 6× 0.07672119140625 + 7× 0.2117919921875 + 8× 0.383544921875 + 9× 0.2763671875 )
+0.01× ( 0× 0.0450439453125
+ 1× 0.035614013671875 + 2× 0.050628662109375 + 3× 0.044342041015625 + 4× 0.0400390625 + 5× 0.3515625 + 6× 0.048309326171875 + 7× 0.041961669921875 + 8× 0.04681396484375 + 9× 0.035888671875 ) .
Experimentally though not very common, score smoothing poses a slight issue when LLaVA out-
puts “1.0”. This occurs because only tokens after the decimal point are considered, disregarding the units place. To address this, we assume that the probability of obtaining “0” in the units place corresponds to the likelihood of LLaVA outputting a raw score of 0.9. Consequently, the final score is determined by the sum of the probability of obtaining “0” in the units place multiplied by 0.9, and the probability of obtaining “1” in the units place.

heading: C Datasets
text: Flickr8k-Expert (Hodosh et al., 2013) consists of 5,664 image-caption pairs with 1,000 images, each image having 4 or 5 corresponding reference captions. Three expert annotators evaluate each imagecaption pair on a scale from 1 (not a match) to 4 (perfect match).
Flickr8k-CF (Hodosh et al., 2013) comprises 47,830 image-caption pairs with 1,000 images, each having approximately 5 reference captions. An evaluation by three annotators involves determining whether the image-caption pair is a match, categorized as either “yes” or “no”. The score for each pair is the proportion of “yes” responses.
COMPOSITE (Aditya et al., 2015) contains 3,995 images sourced from Flickr8k (997 images), Flickr30k (991 images; Young et al., 2014), and MSCOCO (2,007 images; Lin et al., 2014). Each image is associated with 3 candidate captions and approximately 5 reference captions. Image-caption pairs in COMPOSITE are scored on a scale from 1 (not a match) to 5 (perfect match).
Pascal-50S (Vedantam et al., 2015) includes 4,000 caption pairs with 1,000 images, and a label indicating which of the two captions is deemed correct by 48 annotators. Approximately 50 reference captions are linked to each image. The caption pairs in Pascal-50S are categorized based on the composition of the two captions: HC denotes two correct human-written captions; HI indicates two human-written captions with one correct and one incorrect; HM represents one human-written caption and one machine-generated caption; MM signifies two machine-generated captions.
FOIL (Shekhar et al., 2017) is created from 32,150 images extracted from MSCOCO. It consists of 99,480 image-caption pairs, where the captions are made as follows: one from the MSCOCO captions of the image, and the other by replacing one noun in the selected caption with an incorrect but similar word.

heading: D More Examples of Explanations
text: Figure 6 provides additional examples of explanations generated by LLaVA for FLEUR. Table 7 displays the complete explanations that are omitted in the main paper’s figures, indicated by ‘...’ notation. Table 8 shows more examples of explanations for FLEUR and explanations for CLAIR.

heading: E Failure Cases
text: Figure 7 illustrates cases where the explanations generated by LLaVA for FLEUR are inconsistent with reality. Analyzing instances in Flickr8kExpert where FLEUR’s judgment does not align with human judgment, two types of errors can be identified. First, samples where LLaVA fails to accurately comprehend the image, as seen in the top of Figure 7 where it appears to have mistaken a dog’s blue clothing for a frisbee. Second, samples where the model exhibits bias, as evidenced in the bottom of Figure 7 where LLaVA incorrectly identifies a boy with long hair as a girl. This is speculated to be due to biases introduced during the pretraining of LLaVA.

heading: F Short Description Generation Details
text: We create short descriptions for images in Pascal50S using LLaVA and compare the score difference with reference captions. The prompt used to obtain short descriptions is “Please give me a short caption of the image.” Given LLaVA’s tendency to generate detailed descriptions, we explicitly instruct it to create short captions. When generating captions, we use LLaVA’s hyperparameters with top_p set to 0.7 and temperature set to 0.2.

heading: G Generation Order between Scores and Explanations
text: Using the Flickr8k-Expert dataset, we modify the order of prompts to generate explanations first, followed by scores. Except for modifying the base
instructions to generate explanations before scores, we use the same grading criteria as proposed in FLEUR and apply score smoothing. As a result, generating in explanation-score order achieve τc = 50.3 that do not differ significantly from FLEUR’s result τc = 53.0.

heading: Future Work
text: We leave it to future work