Papers by Seongtae Hong
Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly incorporating multilingual capabilities, fueling the demand to transfer them into target language-specific models. |
| Approach: | They propose a novel cross-lingual transfer technique that recycles embeddings from target language Pre-trained Language Models to transmit deep representational strengths to LLMs. |
| Outcome: | The proposed technique outperforms existing methods in cross-lingual understanding setups and achieves faster convergence and lower loss during language adaptation. |
Cross-Lingual Optimization for Language Transfer in Large Language Models (2025.acl-long)
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| Challenge: | Adapting large language models to other languages often suffers from an overemphasis on English performance. |
| Approach: | They propose a cross-lingual optimization technique that efficiently transfers an English-centric LLM to a target language while preserving its English capabilities. |
| Outcome: | The proposed model outperforms SFT in acquiring target language proficiency and maintaining English performance in low-resource languages. |
Metric Calculating Benchmark: Code-Verifiable Complicate Instruction Following Benchmark for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent frontier-level LLMs have saturated many previously difficult benchmarks, leaving little room for further differentiation. |
| Approach: | They propose a benchmark to evaluate whether LLMs can execute string-matching NLP metrics by strictly following step-by-step instructions. |
| Outcome: | The proposed benchmarks show that they can perform step-by-step execution, instruction adherence, numerical computation, and long-range consistency in handling intermediate results. |
I Don’t Need Solution. I Need Emotional Support : Empathetic LLMs based on Emotional Validation (2026.findings-acl)
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| Challenge: | Existing large language models (LLMs) struggle to generate emotional support response, despite observing and reflecting on the help-seeker’s situation . Empathy drives the formation of constructive interpersonal and supportive relationships, including counseling for mental health care . |
| Approach: | They propose to use a two-stage training process to enhance empathetic response generation through empathy acquisition and emotional validation alignment. |
| Outcome: | The proposed method significantly improves empathetic response generation, achieving superior performance in both automatic and human evaluations. |
CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)
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| Challenge: | Existing multilingual embedding models often struggle to capture cross-lingual alignment during training. |
| Approach: | They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English. |
| Outcome: | The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English. |
REVISE: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy (2025.acl-industry)
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| Challenge: | Existing Document AI frameworks lack the capability to structurally organize and manage document information. |
| Approach: | They propose a framework that corrects OCR errors at the character, word, and structural levels and a synthetic data generation strategy that realistically simulates such errors to train an effective correction model. |
| Outcome: | The proposed framework improves document retrieval and question answering tasks by correcting errors introduced by OCR errors at the character, word, and structural levels. |
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| 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. |
MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have advanced in many fields, but focus on English-centric models requires extensive data. |
| Approach: | They propose a method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddables to target languages. |
| Outcome: | The proposed method outperforms baseline and existing cross-lingual transfer methods in target languages. |
Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning (2024.emnlp-industry)
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Seongtae Hong, Joong Shin, Jaehyung Seo, Taemin Lee, Jeongbae Park, Cho Young, Byeongho Choi, Heuiseok Lim
| Challenge: | Existing off-premise Question-Answering systems based on Large Language Models face data leakage and domain-specific tuning challenges. |
| Approach: | They propose an on-premise intelligent PMS framework based on a chunking method . they propose instruction tuning using relevant domain-specific data improves LLM performance . |
| Outcome: | The proposed framework improves performance even under limited data conditions. |