Papers by Edward Choi
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning (2025.naacl-long)
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| Challenge: | Existing methods for ensembling language models fail to address complex reasoning tasks. |
| Approach: | They propose a framework for process-level ensembling of large language models using Monte Carlo tree search. |
| Outcome: | The proposed framework outperforms both language model decoding and language model ensemble methods on five reasoning benchmarks. |
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. |
Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction (2021.acl-short)
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| Challenge: | Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation systems. |
| Approach: | They propose a method to preserve terminology in translations as lexical constraints with or without a term dictionary at test time. |
| Outcome: | The proposed setup achieves consistent improvements on terminology and sentence-level translation for three domain-specific corpora in two language pairs. |
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)
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| Challenge: | Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics. |
| Approach: | They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations. |
| Outcome: | The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. |
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. |
Do Language Models Understand Measurements? (2022.findings-emnlp)
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| Challenge: | Existing studies on numerical reasoning over text (NRoT) tests PLMs to understand numbers in contexts where numbers are an integral part of the context. |
| Approach: | They propose a simple embedding strategy to better distinguish between numbers and units, which leads to a significant improvement in probing tasks. |
| Outcome: | The proposed model distinguishes between numbers and units, which leads to significant improvement in probing tasks. |
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 . |
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text. |
| Approach: | They propose a pipeline that expands existing evaluation sets by adding alternative valid terms for aspect and opinion. |
| Outcome: | The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets. |
A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant (2025.acl-industry)
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| Challenge: | Empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. |
| Approach: | They develop an LLM-based virtual teaching assistant and deploy it in an introductory AI programming course with 477 graduate students. |
| Outcome: | The proposed system is tested in an introductory AI programming course with 477 graduate students. |
EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records (2024.findings-acl)
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| Challenge: | EHR-SeqSQL is the first text-to-SQl dataset to include sequential and contextual questions. |
| Approach: | They propose a sequential text-to-SQL dataset for electronic health records databases that addresses critical yet underexplored aspects in text- to-SqL parsing. |
| Outcome: | The proposed dataset improves compositional generalization efficiency and improves interactivity and compositionality. |
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. |
Open-WikiTable : Dataset for Open Domain Question Answering with Complex Reasoning over Table (2023.findings-acl)
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| Challenge: | Open-WikiTable is the first open domain question answering dataset that requires complex reasoning over tables. |
| Approach: | They propose to use open-domain question answering over tables to extract questions from tables. |
| Outcome: | The dataset is publicly available. it is built upon WikiSQL and WikiTableQuestions. |
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. |