| Challenge: | Figure and non-literal expressions are deeply integrated in human communication . text-to-image models like Stable Diffusion struggle to depict non-figural expression . |
| Approach: | They propose a series of lightweight and robust language models that can be used to visualise non-literal expressions. |
| Outcome: | The proposed language models are more robust than existing models and can generate high-quality images. |
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| Challenge: | Existing text-to-image frameworks for figurative illustration rely on proprietary models or human supervision to achieve adequate alignment. |
| Approach: | They propose a critique-driven framework that uses VLM feedback to refine visual elaborations for figurative image generation. |
| Outcome: | The proposed framework outperforms existing figurative image-to-text pipelines on human-supervised visual elaborations. |
Computational Narrative Understanding for Expressive Text-to-Speech (2026.findings-acl)
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| Challenge: | Recent advances in text-to-speech systems have been driven by large, multi-domain speech corpora. |
| Approach: | They propose a large-scale 5.3K hours of expressive speech drawn from character quotations . they fine-tune a flow-matching model and train from scratch . |
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From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons (2026.acl-long)
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| Challenge: | Autoregressive (AR) models rely on bidirectional attention, creating a structural mismatch with pre-trained Autoregression models. |
| Approach: | They propose a framework that efficiently adapts autoregressive (AR) models to the diffusion paradigm. |
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FLUTE: Figurative Language Understanding through Textual Explanations (2022.emnlp-main)
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| Challenge: | Figurative language understanding is a recognizing textual entailment task, but lacks data for figurative language. |
| Approach: | They propose to use a dataset to analyze figurative NLI instances with explanations to improve models' performance. |
| Outcome: | The proposed dataset can scale up models even for figurative language using human annotations. |
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
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| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
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Learning Visually-Grounded Semantics from Contrastive Adversarial Samples (C18-1)
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| Challenge: | Existing frameworks for grounding distributional representations of texts on the visual domain are limited . effective and efficient grounding of distributional embeddings remains challenging . |
| Approach: | They propose to ground distributional representations of texts on the visual domain using visual-semantic embeddings. |
| Outcome: | The proposed model improves on a diverse set of downstream tasks and defends known-type adversarial attacks. |
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization (2022.findings-emnlp)
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| Challenge: | Combining visual modality with pretrained language models has been effective for descriptive tasks such as image captioning. |
| Approach: | They ask: do multimodal models combine visual and visual adapted language models? they find that CLIP image representations and scaling of language models do not consistently improve self-rationalization in multimodal tasks. |
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MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
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Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
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VisText: A Benchmark for Semantically Rich Chart Captioning (2023.acl-long)
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| Challenge: | Current approaches for automatically generating chart captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns). |
| Approach: | They propose a dataset of 12,441 pairs of charts and captions that describe charts’ construction, report key statistics, and identify perceptual and cognitive phenomena. |
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On the Consistency of Commonsense in Large Language Models (2025.findings-acl)
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| Challenge: | Existing evaluations of commonsense for large language models focus on downstream knowledge tasks, failing to probe whether LLMs truly understand and utilize knowledge or merely memorize it. |
| Approach: | They propose to automatically construct a large benchmark named CoCo which measures LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
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