Papers by Sungjin Park
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning (2025.naacl-long)
Copied to clipboard
| 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. |
Learning Slice-Aware Representations with Mixture of Attentions (2021.findings-acl)
Copied to clipboard
| Challenge: | Real-world machine learning systems are achieving excellent performance in terms of coarse-grained metrics like overall accuracy and F-1 score. |
| Approach: | They extend slice-based learning (SBL) with a mixture of attentions to learn slice-aware dual attentive representations. |
| Outcome: | The proposed approach outperforms the baseline method and the original SBL approach on monitored slices with two natural language understanding tasks. |
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems (2021.emnlp-main)
Copied to clipboard
Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya
| Challenge: | Existing methods to improve NLU are laborintensive and expensive. |
| Approach: | They propose a scalable and automatic approach to improving NLU in a large-scale conversational AI system by leveraging implicit user feedback. |
| Outcome: | The proposed framework improves NLU in a large-scale conversational AI system across 10 domains. |
Do Language Models Understand Measurements? (2022.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
| 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 . |
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue (2022.lrec-1)
Copied to clipboard
| Challenge: | FreeTalky is a deep learning-based foreign language learning platform for people who experience anxiety dealing with foreign languages. |
| Approach: | They propose a deep learning-based foreign language learning platform called FreeTalky . it employs a humanoid robot NAO and various deep learning models . |
| Outcome: | The proposed system provides personalized learning based on persona dialogue and grammar error correction, and also helps alleviate xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation. |
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
| Approach: | They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems. |
| Outcome: | The proposed model improves QA performance and retrieval relevance and contextual understanding. |
Open World Classification with Adaptive Negative Samples (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models with no effective open category data during training are limited by the lack of effective open categories data during the training stage. |
| Approach: | They propose an approach to generate effective open category samples in the training stage and without requiring prior knowledge or external datasets. |
| Outcome: | The proposed approach generates effective synthetic open category samples in the training stage and without requiring any prior knowledge or external datasets. |