Papers by Beakcheol Jang
UniRAG: A Unified RAG Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting (2025.findings-emnlp)
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| Challenge: | Existing retrieval-augmented generation approaches struggle with query complexity, propagated reasoning errors, or rely on incomplete or noisy retrieval. |
| Approach: | a unified retrieval-augmented generation framework is developed to address query complexity . the framework decomposes queries into semantically coherent sub-queries . it explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps. |
| Outcome: | a new framework improves answer completeness and reliability by decomposing queries into coherent sub-queries . the framework explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps. |
CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning (2026.findings-acl)
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| Challenge: | Existing time series forecasting approaches reduce them to numerical predictors that bypass their strengths or allow direct forecast generation that destabilizes predictions in non-stationary settings. |
| Approach: | They propose a framework that decouples semantic reasoning from quantitative prediction. |
| Outcome: | The proposed framework decouples semantic reasoning from quantitative prediction. |
PROGRAM: Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries (2026.findings-acl)
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| Challenge: | Current retrieval-augmented generation methods struggle with complex multi-hop reasoning, relying on unstructured semantic matching that lacks the logical structure needed to systematically guide retrieval. |
| Approach: | They propose a framework that elevates retrieval to structured, program-guided reasoning by combining three stages of program-type selection and evidence accumulation. |
| Outcome: | Evaluated on five benchmarks including HotPotQA, 2WikiMultihopQA, ARC-Challenge, MMLU-Pro, and MedQA with various LLMs, PROGRAM achieves state-of-the-art performance with up to 24% relative improvement on HotPtQA and 13.2% on MedQA over strong baselines including FLARE, ProbTree and Self-RAG. |