Papers by Paul Hongsuck Seo
ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025.findings-emnlp)
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| Challenge: | a prior graph-based approach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. |
| Approach: | They propose a RetrievalEnhanced, Topic-Augmented Graph framework that retrieves relevant summaries from a topic. |
| Outcome: | The proposed framework improves response quality while significantly reducing inference time compared to the baseline. |
LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs (2025.naacl-long)
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| Challenge: | Large language models excel in generating coherent and contextually rich outputs, but their capacity to handle long-form contexts is limited by fixed-length position embeddings. |
| Approach: | They propose a method that enables the efficient processing long-form sequences beyond the model’s length limit through recurrent compression without retraining the entire model. |
| Outcome: | The proposed method significantly improves LLM’s ability to manage extended contexts, making it well-suited for tasks that require both comprehensive context understanding and query relevance. |
ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision (2025.acl-long)
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| Challenge: | Multi-hop question answering requires reasoning across multiple documents to answer complex questions. |
| Approach: | They propose a method for training dense retrievers for multi-hop question answering . they leverage large language models to measure document-question relevance with answer consistency . their results lead to state-of-the-art Exact Match and F1 scores for MHQA . |
| Outcome: | Evaluated on three MHQA benchmarks, the proposed method improves retrieval performance . it leads to state-of-the-art Exact Match and F1 scores for the proposed technique . |
GOAT: A Training Framework for Goal-Oriented Agent with Tools (2026.findings-acl)
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| Challenge: | Recent advances in large language models have led to remarkable progress across a wide range of natural language processing tasks. |
| Approach: | They propose a training framework that enables fine-tuning LLM agents without human annotation. |
| Outcome: | The proposed framework enables fine-tuning LLM agents without human annotation. |