Papers by Na Min An
Capturing the Relationship Between Sentence Triplets for LLM and Human-Generated Texts to Enhance Sentence Embeddings (2024.findings-eacl)
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| Challenge: | Recent advances in building sentence embedding models have centered on replacing traditional human-generated text datasets with those generated by LLMs. |
| Approach: | They propose a loss function that incorporates Positive-Negative sample Augmentation within the contrastive learning objective to enhance sentence embeddings using both human and LLM-generated datasets. |
| Outcome: | The proposed model mitigates the sentence anisotropy problem in Wikipedia corpus and improves Spearman’s correlation in standard Semantic Textual Similarity (STS) tasks (+1.47% compared to CLHAIF). |
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions (2025.acl-long)
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| Challenge: | Existing studies show that direct generation of diagram descriptions is costly and biased against blind and low-vision (BLV) users. |
| Approach: | They ask sighted individuals to assess diagram descriptions generated by vision-language models . they use latent supervision to guide the models with latent inference . |
| Outcome: | The results show that visual descriptions generated by vision-language models are effective and useful to educators who are themselves BLV and teach visually impaired learners. |
Image Embedding Sampling Method for Diverse Captioning (2025.emnlp-main)
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| Challenge: | Currently, large-scale captioning models are less accessible for resource-constrained applications such as mobile devices and assistive technologies. |
| Approach: | They propose a training-free framework that enhances caption diversity and informativeness by explicitly attending to distinct image regions using a comparably small VLM as the backbone. |
| Outcome: | The proposed framework achieves comparable performance to larger models on MSCOCO, Flickr30k, and Nocaps test datasets while maintaining strong image-caption relevancy and semantic integrity with the human-annotated captions. |
Diffusion Models Through a Global Lens: Are They Culturally Inclusive? (2025.acl-long)
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Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, Eunsu Kim, Junyeong Park, James Thorne, Alice Oh
| Challenge: | Text-to-image diffusion models have produced compelling, detailed images from text prompts, but their ability to accurately represent cultural nuances remains an open question. |
| Approach: | They propose a benchmark to evaluate whether diffusion models can generate culturally specific images spanning ten countries. |
| Outcome: | The proposed model fails to generate culturally specific images spanning ten countries . it shows significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images. |
I0T: Embedding Standardization Method Towards Zero Modality Gap (2025.acl-long)
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| Challenge: | Recent studies on Contrastive Language-Image Pretraining suffer from a *modality gap* . modality gap occurs when image and text embeddings are projected to disparate manifolds . |
| Approach: | They propose a framework that reduces the modality gap by adding two normalization layers to each encoder. |
| Outcome: | The proposed framework reduces the modality gap while preserving the original embedding representations of trained models with their locked parameters. |
Can Large Language Models Capture Dissenting Human Voices? (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. |
| Approach: | They evaluate the performance and alignment of large language models with humans using Monte Carlo Estimation and Log Probability Estimationic methods to estimate the multinomial distribution. |
| Outcome: | The proposed models fail to capture human disagreement distribution and inference and human alignment performance plunge even further on data samples with high disagreement levels raising concerns about their natural language understanding ability and representativeness to a larger human population. |
Stable Language Model Pre-training by Reducing Embedding Variability (2024.emnlp-main)
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| Challenge: | Stable pre-training is essential for achieving better-performing language models, but tracking pre-train stability is impractical due to high computational costs. |
| Approach: | They propose to use Token Embedding Variability as a proxy to estimate pre-training stability. |
| Outcome: | The proposed method improves stability and lowers perplexities even at deeper layer counts. |