Papers by Edward Kim
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles (2025.findings-emnlp)
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Ho Yin Sam Ng, Edward Hsu, Aashish Anantha Ramakrishnan, Branislav Kveton, Nedim Lipka, Franck Dernoncourt, Dongwon Lee, Tong Yu, Sungchul Kim, Ryan A. Rossi, Ting-Hao Kenneth Huang
| Challenge: | Figure captions are crucial for helping readers understand and remember a figure’s key message. |
| Approach: | They propose a dataset for personalized figure caption generation with multimodal figure profiles that provide inputs and profiles for each figure . |
| Outcome: | The proposed dataset provides inputs and profiles for personalized figure caption generation with multimodal figure profiles. |
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
| Approach: | They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs. |
| Outcome: | The proposed framework outperforms fully-supervised models in KG-based fact verification and KGQA benchmarks. |
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes (2024.findings-acl)
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Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi
| Challenge: | Clinical notes are an extensive repository of information specific to individual patients. |
| Approach: | They create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature and train a clinical large language model, Asclepius. |
| Outcome: | The proposed model outperforms several other models and is supported by detailed evaluations conducted by GPT-4 and medical professionals. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)
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Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo
| Challenge: | Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics. |
| Approach: | They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself. |
| Outcome: | The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics. |
LayerNorm Induces Recency Bias in Transformer Decoders (2026.findings-acl)
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| Challenge: | Existing studies show that stacking causal self-attention layers alone induces a positional bias in attention scores toward earlier tokens, but this differs from the bias toward later tokens observed in Transformer decoders, known as recency bias. |
| Approach: | They propose to stack causal self-attention layers and layer norm to induce recency bias in Transformer decoders by analyzing the interaction between causal self and other architectural components. |
| Outcome: | The proposed method provides new theoretical insights into how positional information interacts with architectural components and suggests improvements in positional encoding strategies. |
Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity (2022.coling-1)
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| Challenge: | obtaining large amounts of human-annotated datasets to train a sentence embedding model is difficult and expensive. |
| Approach: | They propose to train a classifier that identifies machine-written sentences and then use it to train an embedding model on synthetic data. |
| Outcome: | The proposed method outperforms baselines on four real-world datasets and generalizes well. |
Specializing Multi-domain NMT via Penalizing Low Mutual Information (2022.emnlp-main)
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| Challenge: | Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. |
| Approach: | They propose a method that penalizes low MI to be higher for domain-specific NMTs. |
| Outcome: | The proposed method achieves state-of-the-art performance among current models . it also promotes low MI to be higher resulting in domain-specialized multi-domain NMT. |
KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) must possess an understanding of the nation’s culture and basic knowledge. |
| Approach: | They propose to construct a national alignment benchmark, KorNAT, which measures the alignment between an LLM and a targeted country from two perspectives: social value alignment and common knowledge alignment. |
| Outcome: | The proposed model passes the national alignment score of 7 LLMs, indicating there is room for improvement. |
Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs (2025.emnlp-main)
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| Challenge: | Ambiguity is pervasive in real-world questions, yet large language models often respond with confident answers rather than seeking clarification. |
| Approach: | They show that question ambiguity is linearly encoded in the internal representations of large language models (LLMs) by training linear probes, they identify sparse sets of Ambiguity-Encoding Neurons (AENs) |
| Outcome: | The proposed model outperforms prompting-based and representation-based baselines on ambiguity detection and generalization. |