Papers by Joohyung Lee
LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning (2026.findings-acl)
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| Challenge: | Recent large language models struggle with high computational costs and logical inconsistencies . a framework that translates natural language into Answer Set Programming (ASP) is developed . |
| Approach: | They propose a framework that translates natural language into Answer Set Programming (ASP) stable model semantics allow LLMs to express default rules and exceptions, they show . |
| Outcome: | The proposed framework outperforms existing methods on nonmonotonic reasoning tasks without any per-task engineering and applies uniformly across reasoning tasks. |
Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs (2025.acl-long)
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Anshumann Anshumann, Mohd Abbas Zaidi, Akhil Kedia, Jinwoo Ahn, Taehwak Kwon, Kangwook Lee, Haejun Lee, Joohyung Lee
| Challenge: | Knowledge distillation is a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. |
| Approach: | They propose an importance-sampling-based method which provides unbiased estimates, preserves the gradient in expectation, and requires storing significantly sparser logits. |
| Outcome: | The proposed method enables faster training of student models with marginal overhead (10%) compared to cross-entropy based training, while maintaining competitive performance compared with full distillation. |
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text (2023.findings-acl)
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| Challenge: | Large language models (LLMs) are robust and general, but their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems. |
| Approach: | They propose to use large language models as a few-shot semantic parser to convert natural language sentences into a logical form that serves as input for answer set programs. |
| Outcome: | The proposed model can handle multiple question-answering tasks without requiring retraining for each new task. |
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval (2025.acl-long)
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| Challenge: | Existing methods for table-text retrieval are limited due to the need to bridge structured tables and unstructured passages. |
| Approach: | They propose a table-text retrieval system that combines the strengths of both approaches . they propose bipartite subgraph retrieval and query-relevant node expansion . |
| Outcome: | The proposed method outperforms state-of-the-art models with a 42.6% and 39.9% improvement on the OTT-QA benchmark. |
LILaC: Late Interacting in Layered Component Graph for Open-domain Multimodal Multihop Retrieval (2025.emnlp-main)
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| Challenge: | Existing multimodal document retrieval frameworks focus on textual, tabular, and visual elements, but there is a shift toward open-domain multimodal retrieval. |
| Approach: | They propose a multimodal retrieval framework that uses a component graph and a late-interaction-based subgraph retrieval method to capture semantic relationships between components. |
| Outcome: | The proposed framework achieves state-of-the-art retrieval performance on all five benchmarks . it is based on a layered component graph representing multimodal information at two layers . |