Papers by Hannah Kim
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks (2024.findings-acl)
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| Challenge: | Large language models generate fluent text with minimal task-specific supervision, but their ability to generate rationales for knowledge-intensive tasks (KITs) remains under-explored. |
| Approach: | They propose to generate retrieval-augmented rationalization of KIT model predictions via external knowledge guidance within a few-shot setting. |
| Outcome: | The proposed rationales were compared with crowd-sourced rationale models on factuality, sufficiency, and convincingness. |
A Dynamic Self-Evolving Extraction System (2026.acl-demo)
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| Challenge: | High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. |
| Approach: | They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text. |
| Outcome: | The proposed toolkit continuously improves as it is used in medical, legal, and HR domains. |
RECAP: REwriting Conversations for Intent Understanding in Agentic Planning (2026.findings-eacl)
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| Challenge: | Understanding user intent is essential for effective conversational assistants . however, real-world dialogues are often ambiguous, underspecified, or dynamic . |
| Approach: | They propose a benchmark to evaluate intent rewriting in user-agent dialogues . they propose rewriters that reframe user-goal dialogues into concise representations of user goals . |
| Outcome: | The proposed benchmark outperforms baselines in terms of plan preference and fine-tuning two DPO-based rewriters yields additional utility gains. |
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems (2025.emnlp-demos)
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| Challenge: | Large language models (LLMs) are being used for planning in orchestrated multi-agent systems . existing LLMs fall short of human expectations and lack effective mechanisms for users to inspect, understand, and control their behaviors. |
| Approach: | They propose a system supporting human-in-the-loop planning through conversational and graph-based interfaces. |
| Outcome: | AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows. |
Low-resource Interactive Active Labeling for Fine-tuning Language Models (2022.findings-emnlp)
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| Challenge: | Existing active learning methods for fine-tuning language models are underperforming in low-resource, interactive labeling setting. |
| Approach: | They propose a novel active learning method that employs a hybrid sampling strategy to minimize labeling cost and acquisition latency while providing a framework for adapting to dataset diversity. |
| Outcome: | The proposed method reduces labeling cost and acquisition latency while providing a framework for adapting to dataset diversity via user guidance. |
MEGAnno+: A Human-LLM Collaborative Annotation System (2024.eacl-demo)
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| Challenge: | Large language models (LLMs) can label data faster and cheaper than humans . however, they may fall short in understanding of complex contexts, leading to incorrect labels . |
| Approach: | They propose a collaborative approach where humans and LLMs work together to produce reliable labels. |
| Outcome: | The proposed system can produce reliable and high-quality labels faster and cheaper than humans . compared to traditional models, it can generate labels faster, at a lower cost . |