RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Introduction
Figurative language is integral to human communication, enabling a variety of communicative goals Roberts and Kreuz ( 1994 ) , including affective communication Fussell and Moss ( 2014 ) . Figurative language
presents a significant challenge to computational approaches as it requires understanding of implicit meaning behind an expression Stowe et al. ( 2022 ); Shutova ( 2011 ); Veale et al. ( 2016 ); Zhou et al. ( 2021 ) .
Recently, Chakrabarty et al. ( 2022 ) proposed a task and dataset for Figurative Language Understanding through Textual Explanations (FLUTE) that frames the problem as an explainable textual entailment covering a variety of figurative language phenomena in text: metaphors, similies, idioms, and sarcasm. This dataset has been used successfully to advance and benchmark the capabilities of LLMs for understanding figurative language in text Saakyan et al. ( 2022 ); Ziems et al. ( 2024 ); Sravanthi et al. ( 2024 ); Dey et al. ( 2024 ) .

However, figurative meaning is also prevalent in visual phenomena, such as visual metaphors Akula et al. ( 2023 ); Chakrabarty et al. ( 2023 ) , multimodal sarcasm Desai et al. ( 2022 ) , and humor Hessel et al. ( 2023 ); Hwang and Shwartz ( 2023 ) . Yet so far most of the work on vision and language models (VLMs) has focused on understanding literal meaning in images and captions (e.g., ScienceQA Lu et al. ( 2022 ) , MMMU Yue et al. ( 2024 ) ) including work on explainable visual entailment Kayser et al. ( 2021 ) . Building on the idea of FLUTE Chakrabarty et al. ( 2022 ) for text, we present a new dataset for visual figurative language understanding with textual explanations (V-FLUTE). Our dataset contains 6,027 < image, claim, label, explanation > instances spanning diverse figurative phenomena.
Each instance contains an image (premise) and a textual claim (hypothesis) that is either entailed or contradicted by the image. Deciding the entailment relation requires the vision-language model to understand the implicit meaning in both the visual and textual modalities. Our dataset contains figurative phenomena present in the image, in the caption, or in both. In addition, to mitigate the dependence on spurious correlations, to more rigorously investigate reasoning capabilities, and to promote explainability, our task requires the model to generate a plausible explanation for the output label. See Figure 1 for two examples from our dataset.

We make the following contribution towards assessing VLMs ability to understand multimodal figurative phenomena:

V-FLUTE, a high-quality dataset of 6,027 < image, claim, label, explanation > instances
built using a human-LLMs collaboration framework covering several phenomena: metaphors, similies, idioms, sarcasm, and humor (Section 3 ). We will make the dataset available.

A suite of evaluations to assess current VLMs’ capabilities on this new task of explainable visual figurative entailment (Section 4.2 and 4.3 ).

A detailed human evaluation with error analysis yielding insights into types of errors for different classes of models (Section 5 ).
Related Work
Textual entailment MacCartney and Manning ( 2008 ); Bowman et al. ( 2015 ) and visual entailment Xie et al. ( 2019 ) tasks have been proposed to measure language and multimodal understanding.
However, models trained to simply improve label accuracy on these data can be brittle and suffer from spurious correlations Poliak et al. ( 2018 ); Gururangan et al. ( 2018 ); McCoy et al. ( 2019 ); Gardner et al. ( 2021 ) .
Datasets such as e-SNLI Camburu et al. ( 2018 ) and e-SNLI-VE Kayser et al. ( 2021 ) augment existing entailment datasets with natural language explanations and train models to not only predict the label, but also generate a textual explanation for the reason behind the prediction.
Such approach has been further adopted for a variety of tasks, such as commonsense reasoning Rajani et al. ( 2019 ); Aggarwal et al. ( 2021 ) and social norm understanding CH-Wang et al. ( 2023 ) among others Wiegreffe and Marasovic ( 2021 ) .
This approach has been extended to assess LLMs’ capabilities on understanding figurative language through the FLUTE dataset Chakrabarty et al. ( 2022 ) . FLUTE frames figurative language understanding as an explainable textual entailment task.
Recent progress in multimodal models Li et al. ( 2022 ); Alayrac et al. ( 2022 ); OpenAI ( 2023 ); Team et al. ( 2023 ); Liu et al. ( 2023b ); Anthropic ( 2024 ) prompts us to asses similar capabilities when extended to multimodal setting, testing the understanding of non-literal meaning contained in both images and text.
We present an equivalent of the FLUTE dataset for the visual modality: V-FLUTE.

A number of previous works has focused on modeling figurative phenomena beyond text. Chakrabarty et al. ( 2023 ) use a human-AI collaboration framework to generate visual metaphors from linguistic metaphors (HAIVMet dataset) and propose
a visual entailment task as an extrinsic evaluation of dataset quality. The dataset contains images, claims, and labels, but no textual explanations. Yosef et al. ( 2023 ) proposed a benchmark (IRFL) where given an idiom, metaphor, or simile the model has to distinguish which of the four associated images implies the figurative meaning of the expression. This dataset focuses on the figurative meaning in the textual modality and does not contain textual explanations. There has also been work on understanding multimodal sarcasm with explanations Desai et al. ( 2022 ) , mostly containing noisy user-generated text and crowdworker-written explanations. Other line of work has focused on understanding humor with multimodal models. MemeCap Hwang and Shwartz ( 2023 ) is a dataset for understanding memes. Hessel et al. ( 2023 ) release a corpus of annotated New Yorker Caption Contest entries, where the goal is to come up with a humorous captions for an image, with high-quality explanations for why the caption is humorous. The dataset is relatively limited in size containing only 520 unique instances in its training set. We leverage all these benchmarks to build V-FLUTE.
V-FLUTE Task and Dataset
Following prior work on figurative language understanding in text defined as explainable textual entailment Chakrabarty et al. ( 2022 ) , we define the visual figurative understanding as an explainable visual entailment task : given an image (premise) p and a claim (hypothesis) h , output a textual explanation \hat{e} justifying whether the premise entails or contradicts the hypothesis and assign a label \hat{y}\in\{\texttt{Entailment},\texttt{Contradiction}\} . We focus on the binary classification task since, for neutral labels, the explanations would be trivial (simply describing the image).

To build V-FLUTE, we start with existing multimodal figurative datasets and use human-AI collaboration frameworks with expert annotators Chakrabarty et al. ( 2022 ); Wiegreffe et al. ( 2022 ); Liu et al. ( 2022 ) to transform them into a high-quality, explainable visual entailment benchmark. These datasets cover particular phenomena such as metaphors, similes, idioms, sarcasm or humor. Each instance
includes an image and a caption and the figurative phenomenon can be either in the image, the caption or in both.
We transform each data into a unified < image, claim, label, explanation > format for explainable visual entailment.
An overview of the dataset and our contributions can be found in Table 1 . See examples from each dataset in Table 2 . Below, we describe the construction of V-FLUTE for each figurative language type (metaphors & similes, idioms, sarcasm and humor).
Metaphors and Similes
Metaphors and similes are powerful rhetorical devices that can be expressed either in text or visually in an image. Visual metaphors are used as persuasive devices
in various fields such as advertising Forceville ( 2002 ); Scott ( 1994 ) .
To create visual entailment instances containing metaphors and similes in V-FLUTE, we rely on two existing resources: HAIVMet Chakrabarty et al. ( 2023 ) and IRFL Yosef et al. ( 2023 ) . Instances taken from HAIVMet contain the metaphor/simile as a part of the premise (image), while those taken from IRFL have the metaphor/simile as a part of the hypothesis (text).
HAIVMet as Data Source
The HAIVMet Chakrabarty et al. ( 2023 ) data consists of 1,193 images of visual metaphors spanning over 958 distinct linguistic metaphors. Each image is associated with a claim that can be contradicting or entailing the image. In addition, each image is associate with a visual elaboration that presents a textual description of the image (See Figure 2 ). This visual elaboration was used in the original paper to generate the visual metaphors (images).

Generating Textual Explanations. We augment the dataset with candidate textual explanations. We prompt ChatGPT ( gpt-3.5-0914 ) to generate an explanation for every tuple < visual elaboration, claim, label > (See Figure 2 ; and prompt in Appendix D.1.1 ).

Expert Verification. Each claim is paired with up to 5 images. However, since these images were automatically generated with DALLE-2 using the visual elaborations, not all are completely faithful. Moreover, some claims and labels were inconsistent. Finally, automatically generated LLM candidate explanations are not always correct and require refining. To tackle these issues, we employ an expert verification process involving three expert annotators with significant experience in figurative language and visual metaphor understanding. Since each claim can be paired with more than one visual metaphor, we ask annotators to select the visual metaphor most faithful to the linguistic metaphor and visual elaboration (see Image Selection in Figure 2 ) or select none in the rare case when none of the visual metaphors are of good quality. As a part of the same annotation round, we also ask them to verify and edit the explanation if necessary to ensure correctness and high quality. Post strict quality control, we have 857 < image, claim, label, explanation > instances.
IRFL as Data Source
The IRFL dataset Yosef et al. ( 2023 ) contains 1,440 figurative expressions, each associated with 4 distinct images. One of those images represents the figurative expression (see Figure 3 ), and the other 3 act as distractors.

Image Selection. We automatically select images using CLIP Radford et al. ( 2021 ) . We select one of the distractor images that have the highest CLIPScore ( clip-vit-base-patch16 ) with the corresponding entailing image to create a challenging, contradictory instance (see where an unrelated image of a house is discarded when selecting the contradiction instance in Figure 3 ).

Generating Textual Explanations. We prompt GPT-4 ( gpt-4-vision-preview ) with the ground truth label, claim, and the image to explain the relationship between the image and the claim.

Expert Verification. We recruit the same three expert annotators from HAIVMET annotations and ask them to verify the explanation is adequate and edit it when necessary. We also ask the annotator to discard rare noisy instances where the claim, image, and label do not fit. Post strict quality control, we are left with 1149 < image, claim, label, explanation > instances.
Idioms
The IRFL dataset contains idioms in addition to metaphors and similies. An identical procedure to the one described in Section 3.1.2 was used for generating V-FLUTE instances for idioms (370 < image, claim, label, explanation > examples).
Sarcasm
To create visual entailment instances containing sarcasm, we rely on the MuSE data Desai et al. ( 2022 ) . Similarly to IRFL, instances from MuSE data contain sarcasm in the hypothesis (text).
MuSE as Data Source
The MuSE dataset Desai et al. ( 2022 ) consists of 3510 distinct images, the respective sarcastic claims that act as contradiction instances (see example in Figure 4 ), and crowd worker written explanations justifying the contradiction.

Generating Entailment Claims. Since the dataset only contains sarcastic instances, there are no claims with an entailment relationship. We generate the entailing claims by prompting GPT-4 to generate a non-sarcastic version of the claim while maintaining the user-generated informal style of the text (see the generated entailment claim in Figure 4 ).

Generating Textual Explanations. While the dataset already contains crowdworker-written explanations, upon inspection, they were often deemed poor quality, lacking enough details, and formulaic (e.g., see the crowdworker explanation in Figure 4 ). To improve their quality, we use the dataset’s existing crowdworker explanations and prompt GPT-4 to rewrite and generate high-quality candidate textual explanations given the claim and the label (see the re-written explanation in Figure 4 ). See the prompt in Appendix D.3 .

Expert Verification. Each image is now paired with a GPT-4-generated entailing claim, an original contradicting claim, and their respective labels and explanations. The same three expert annotators checked if the generated explanations are adequate (i.e., complete, correct, and concise) and if not, edit them. Experts were also instructed to discard noisy examples, e.g. when the image does not contradict the sarcastic claim. Through strict quality control, we obtain 1,042 < image, claim, label, explanation > instances.
Humor
For multimodal humor, we rely on two datasets: MemeCap Hwang and Shwartz ( 2023 ) and New Yorker cartoons Hessel et al. ( 2023 ) .
MemeCap as Data Source
This dataset consists of memes along with their captions that describe the meme poster’s intent (see example in Figure 5 ). Memes frequently contain implicit, non-literal meaning Lestari ( 2019 ) and rely on visual metaphors Piata ( 2016 ) , posing a challenge to VLMs.

Claim Generation. Since meme captions are not suited for an entailment task, we perform prompt GPT-4 with the caption to generate a claim from it (see example in Figure 5 ). We filter these set of samples further with GPT-4 by asking whether the image entails the claim and only selecting positive instances. In addition to generating claims that entail the meme, we generate counterclaims using GPT-4.

Generating Textual Explanations. We prompted GPT-4 with the ground truth label in the prompt to explain the relationship between the image and the claim. See prompts in Appendix D.4 .

Expert Verification. We hire the same three expert annotators to ensure the correctness of the data. Each annotator is tasked with verifying that 1) the generated claim fits the image and 2) the explanation is correct and complete, and if not, make the necessary changes. We also ask to discard samples with inappropriate content.
After careful quality control, we have 1958 < image, claim, label, explanation > instances.
NYCartoons as Data Source
The NYCartoons dataset Hessel et al. ( 2023 ) contains 651 high-quality instances from the New Yorker Cartoon Caption Contest. Each instance consists of a humorous image paired with a caption and a natural language explanation justifying the implicit humor between the caption and the image. We simply use the existing data where the caption is treated as a claim entailing the humorous image paired with an explanation.
Dataset Statistics
We split our data into 4,578 training, 726 validation, and 723 testing instances. Detailed counts per phenomenon and dataset, as well as other statistics, are in Appendix A .
Experiments
We empirically study how several baseline models perform on the task of explainable visual entailment. We investigate both off-the-shelf and fine-tuned model performance.
Models
We select a variety of models for our study (see taxonomy in Appendix, Figure 10 ). For off-the-shelf models , we explore both open and API-based models. For open models, we select the (current) state-of-the-art LLaVA-1.6 models Liu et al. ( 2024 ) . LLaVA is one of the simplest, yet one of the most high-performing VLM architectures currently available. It utilizes a pretrained large language model (e.g., Mistral-7B Jiang et al. ( 2023 ) ) and a vision-language cross-modal connector (e.g., an MLP layer) to align the vision encoder (e.g., CLIP Radford et al. ( 2021 ) ) outputs to the language models. We select LLaVA-1.6 models in their 7B and 34B configurations (LLaVA-v1.6-7B and LLaVA-v1.6-34B respectively) and refer to them as LLaVA-ZS-7B and LLaVA-ZS-34B . Both models have been instruction-tuned on less than 1M visual instruction tuning samples to act as general language and vision assistants. It should, however, be noted that these models do not currently support few-shot multimodal prompting.

In addition to zero-shot testing, we also test these models using Compositional Chain-of-Thought Prompting proposed by Mitra et al. ( 2023 ) . The method first prompts the model to generate a scene graph and then utilizes that scene graph in another prompt to answer the relevant question. The method works zero-shot without requiring fine-tuning. We refer to these models as LLaVA-ZS-7B-SG and LLaVA-ZS-34B-SG for the 7B and 34B LLaVA configurations described above.

For API-based models, we select three widely available state-of-the-art VLMs: Claude-3 Opus ( claude-3-opus-20240229 ) Anthropic ( 2024 ) , GPT-4 ( gpt-4-1106-vision-preview ) OpenAI ( 2023 ) and GeminiPro ( gemini-pro-vision ) Team et al. ( 2023 ) . We refer to GPT-4 as the “teacher” model as most candidate explanations were generated with it.

For fine-tuned models, we focus on fine-tuning LLaVA-1.5-7B model Liu et al. ( 2023a ) (the fine-tuning code for 1.6 model is not available during the time the paper was written). To minimize bias for a single instruction, we fine-tune and evaluate the models on a set of 21 instruction paraphrases (see Appendix Table 8 ). Three model configurations are tested:

LLaVA-eViL is a checkpoint of LLaVA-v1.5-7B further fine-tuned on the eViL (e-SNLI-VE) dataset for explainable visual entailment Kayser et al. ( 2021 ) converted to the instruction format. We removed neutral label instances, which resulted in 275,815 training instances and 10,897 validation instances.

LLaVA-VF is the same checkpoint fine-tuned on the training set of V-FLUTE. We also fine-tune the model with a white square instead of the V-FLUTE image (denoted by - Image).

LLaVA-eViL+VF is the same checkpoint fine-tuned on both eViL and V-FLUTE.

All hyperparameters are in Appendix C .
Automatic Metrics
Similarly to prior work Chakrabarty et al. ( 2022 ) we utilize both classic F1 score and an adjusted score that accounts for explanation quality: F1@ExplanationScore.
The ExplanationScore computes the average between BERTScore Zhang* et al. ( 2020 ) based on the microsoft-deberta-xlarge-mnli model He et al. ( 2021 ); Williams et al. ( 2018 ) and BLEURT Sellam et al. ( 2020 ) based on the BLEURT-20 Pu et al. ( 2021 ) .
Since our goal is to ensure models provide an answer for the right reasons, ideally, we would only count predictions as correct when the explanation is also correct. Hence, we report F1@0 (simply F1 score), F1@53 (only predictions with explanation score > 53 are considered correct), and F1@60. Thresholds are selected based on human evaluation of explanation quality in Section 5.3 .
Automatic Evaluation Results
Table 3 shows the results based on the automatic evaluation. We also include results per phenomenon in Appendix F and the drop in performance when accounting for explanations score in Figure 6 . Our results inform the following insights:
Human Baseline
To find out how humans perform on the task, we hire two expert annotators with formal education in linguistics. We present them with 10 example instances and then ask them to complete 99 randomly sampled test set instances. We also evaluate our best model (see Table 3 ) on the same set. Results are shown in Table 4 . Human performance is quite strong, almost reaching 90 F1@0 score overall. Human performance is better than our strongest fine-tuned model (LLaVA-7B-eVil+VF) performance with p<0.05 for Annotator 1 and p<0.07 for Annotator 2. Humans excel at interpreting memes, with both annotators reaching a 100% F1 score. Humans also perform noticeably better on the NYCartoons dataset and on the idiom subset of the task. The model has a slight edge in performance on the sarcasm and visual metaphor subsets of the task, perhaps due to difficulty of these subsets and any potential spurious correlations during fine-tuning.
Human Evaluation and Error Analysis
We conduct human evaluation of generated explanation to more reliably assess their quality and identify key errors in multimodal figurative language understanding. We recruit two expert annotators with background in linguistics for the task and sample 95 random instances from the test set. For each instance, we first provide the annotators with the image, claim and reference explanation and ask the annotators to choose the right label. If the annotator succeeds, they can view the rest of the task, which consists of 3 explanations from our top models by F1@0 in each category: LLaVA-eViL-VF, LLaVA-34B-SG, GPT-4-5shot. The explanations are taken for both correct and incorrect model predictions. For each explanation, we ask whether the explanation is adequate (accurate, correct, complete and concise).
If not, we ask them to identify one of the three main types of errors based on the following taxonomy:

Hallucination: explanation is not faithful to the image, indicating difficulties with basic visual comprehension (see prediction of a blunt tip when the pencil tip is actually sharp in row 1 of Table 5 ).

Unsound reasoning: sentences do not adhere to natural logic or violate common sense (e.g., concluding than an upwards arrow and lots of money imply an economic crisis, see row 3).

Incomplete reasoning: while overall the explanation makes sense, it does not address the key property reasons why the image entails or contradicts the claim (for example, does not address the figurative part in the image, see row 2).

Too Verbose: the explanation is too verbose to the point it would interfere rather than help one decide the correct label.

Error Type Image Claim Label and Explanation Hallucination (describes sharp pencil as having a blunt tip) The tip is as sharp as a knife. Predicted Label: Contradiction Predicted Explanation: The image depicts a pencil with a blunt tip . […] Incomplete (does not address metaphorical meaning of iceberg imagery) They looked at each other in an almost hostile way. Predicted Label: Entailment Predicted Explanation: The image depicts two characters looking at each other with a tense and confrontational expression , which is consistent with the claim […] Unsound ( concludes that large amount of money + upward trending chart implies rough waters ) The world recession of 2001 was one of the worst in history. Predicted Label: Entailment Predicted Explanation: The image depicts a man in a boat, holding a large amount of money and a chart with an arrow pointing upwards. This suggests that the man is navigating through rough waters […]
How Do Models Perform According to Humans?
In Table 6 , we show adequacy and preference rates for explanations from the 3 systems, where an explanation is deemed adequate if both annotators agreed it is, and inadequate if both agreed it is not. The preference percentage is also taken among instances where the annotators agreed that the model’s explanation is preferred among all the adequate explanations. The average IAA using Cohen’s \kappa is 0.47, indicating moderate agreement Cohen ( 1960 ) . We observe that the teacher model is leading in terms of the adequacy of the explanations and preference rate, as expected from a larger system equipped for higher quality reasoning and generation capabilities. Yet still only half of its explanations are considered adequate. This further confirms that despite impressive performance on the F1@0 scores, the models are not yet capable of producing adequate textual explanations in many instances.