Papers by EunJeong Hwang
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models (2024.emnlp-main)
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| Challenge: | Vision-Language Models (VLMs) have shown emerging capabilities through large-scale training that have made them gain popularity in recent years. |
| Approach: | They propose to perform retrieval across universals and cultural visual grounding tasks to assess cultural diversity across universal and culture-specific local concepts. |
| Outcome: | The proposed benchmarks show that the models perform significantly across cultures, underscoring the need for enhancing multicultural understanding in vision-language models. |
MemeCap: A Dataset for Captioning and Interpreting Memes (2023.emnlp-main)
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| Challenge: | a new dataset aims to understand meme captioning tasks using visual metaphors . vision and language models are proving to be effective in image captioning and visual question answering tasks . |
| Approach: | They present a dataset that contains 6.3K memes and 6.3k meme captions . they show that vision and language models still struggle with visual metaphors despite their advanced capabilities . |
| Outcome: | The proposed dataset contains 6.3K memes along with the title of the post containing the meme, meme captions, literal image caption, and visual metaphors. |
A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024.findings-acl)
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| Challenge: | Existing large language models (LLMs) perform poorly in reasoning about subjective knowledge, showing strong biases and lack interpretability requirements. |
| Approach: | They propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. |
| Outcome: | The proposed model outperforms several prominent large language models on the OpinionQA dataset, showing its unique advantages and complementary nature. |
Event-Event Relation Extraction using Probabilistic Box Embedding (2022.acl-short)
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| Challenge: | Existing frameworks of event relation extraction do not guarantee coherence between different relation types, such as anti-symmetry. |
| Approach: | They propose to modify existing ERE framework to guarantee coherence by representing each event as a box representation without applying explicit constraints. |
| Outcome: | The proposed model shows stronger conjunctive constraint satisfaction compared to previous models with constraint injection. |
Aligning Language Models to User Opinions (2023.findings-emnlp)
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| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
| Approach: | They use public opinion surveys to model past user opinions in addition to user demographics and ideology to achieve up to 7 points accuracy gains in predicting public opinions from survey questions. |
| Outcome: | The proposed model achieves 7 points accuracy gains in predicting public opinions from public opinion surveys across a broad set of topics. |
Infusing Theory of Mind into Socially Intelligent LLM Agents (2026.findings-acl)
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| Challenge: | Theory of Mind (ToM) is a key aspect of human social intelligence, yet chatbots and LLMs do not typically integrate it. |
| Approach: | They propose a method that integrates Theory of Mind (ToM) into chatbots and dialogue agents to generate mental states between dialogue turns. |
| Outcome: | The proposed method improves dialogue and social interaction by integrating ToM with dialogue lookahead. |
Knowledge Graph Compression Enhances Diverse Commonsense Generation (2023.emnlp-main)
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| Challenge: | Existing models use commonsense knowledge graphs to extract subgraphs of relevant knowledge pertaining to concepts in the input but due to the large coverage and vast scale of ConceptNet, the extracted subgraph may contain loosely related, redundant and irrelevant information. |
| Approach: | They propose to apply a differentiable graph compression algorithm to extract subgraphs of relevant knowledge from input sentences. |
| Outcome: | The proposed algorithm achieves better quality-diversity tradeoff than a large language model with 100 times the number of parameters. |
BottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle (2025.findings-acl)
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| Challenge: | Humor is an effective communication tool that can manifest in various forms, including puns, exaggerated facial expressions, absurd behaviors, and incongruities. |
| Approach: | They propose a method that elicits relevant world knowledge from vision and language models and refines it to generate an explanation of the humor in an unsupervised manner. |
| Outcome: | The proposed method can be adapted for additional tasks that can benefit from eliciting and conditioning on relevant world knowledge. |
Enhancing Incremental Summarization with Structured Representations (2024.findings-emnlp)
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| Challenge: | Large language models struggle with processing extensive input contexts, leading to redundancy or incoherency. |
| Approach: | They propose a chain-of-key update based on JSON structured memory representations to improve summarization performance by 40% and 14% on two public datasets. |
| Outcome: | The proposed method improves summarization performance by 40% and 14% on two datasets. |