Papers by Hyunjong Ok
Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models (2026.findings-acl)
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| Challenge: | Large language models exhibit surprising sensitivity to structure of the prompt, but mechanisms underlying this sensitivity remain poorly understood. |
| Approach: | They conduct an in-depth investigation on placing context before the questions and options in MCQA prompts. |
| Outcome: | The proposed model outperforms the reverse order (QOC) by over 14%p over a wide range of models and datasets. |
SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities (2024.naacl-long)
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| Challenge: | Named entity recognition (NER) is a task to identify textual spans that correspond to named entities in the given text. |
| Approach: | They propose a model that can generalize to entities unseen during training and handle noisy annotations. |
| Outcome: | The proposed model outperforms existing methods on both MNER and GMNER benchmarks and is robust and accurate. |
Decoding with Limited Teacher Supervision Requires Understanding When to Trust the Teacher (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated their tremendous capability to generate human-like text sentences that convey rich knowledge in various problem domains. |
| Approach: | They propose an algorithm to aggregate small-scale LLM and LLM predictions on initial tokens so that the generated tokens can more accurately condition the subsequent token generation by small-level LLM only. |
| Outcome: | The proposed method improves on the limited supervision scenario on a wide range of models and datasets. |
Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models (2025.findings-acl)
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| Challenge: | Existing approaches to augment language models with audio databases lack the ability to understand auditory signals like humans. |
| Approach: | They propose a method that augments language models with audio databases to generate auditory knowledge . their framework detects multiple audio-related textual spans from the given prompt . |
| Outcome: | The proposed approach achieves state-of-the-art performance on AuditoryBench without external databases. |
Speculative End-Turn Detector for Efficient Speech Chatbot Assistant (2026.acl-long)
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| Challenge: | Spoken dialogue systems with large language models struggle with end-turn detection . this limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations. |
| Approach: | They propose a dataset for end-turn detection that uses a lightweight GRU-based model and a high-performance Wav2vec-based system to make a more challenging classification of distinguishing turn ends from mere pauses. |
| Outcome: | The proposed framework significantly improves real-time ETD accuracy while keeping computations low. |