Papers by Stanley Jungkyu Choi
From Documents to Segments: A Contextual Reformulation for Topic Assignment (2026.findings-acl)
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| Challenge: | Traditional topic modeling treats each document as a single, coherent unit of topic. |
| Approach: | They propose a paradigm that redefines topic assignment at the level of segments . they propose 'segment intrusion task' to extend word intrusion to the span level . |
| Outcome: | The proposed paradigm improves topic purity, interpretability and applicability to multi-theme corpora. |
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning (2024.findings-acl)
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| Challenge: | Recent studies have focused on instruction tuning to show cross-lingual generalization . a novel non-English meta-dataset is used to study instruction tuning . |
| Approach: | They perform instruction tuning individually for two distinct language meta-datasets and assess the performance on unseen tasks in a non-English language. |
| Outcome: | The proposed model outperforms baseline training in English and Korean by 20.7% and 13.6%. |
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination (2024.acl-long)
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| Challenge: | Existing methods to mitigate undesirable biases in instruction-following language models are not effective in accelerating instruction-based learning. |
| Approach: | They propose a method to eliminate bias neurons of language models in instruction-following settings by defining the bias neuron and prove its existence empirically. |
| Outcome: | The proposed method dramatically increases the task performance of language models under zero-shot instruction-following settings without losing the model’s knowledge. |
Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (2024.emnlp-main)
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| Challenge: | Experimental results show that instruction tuning improves zero-shot generalization across various tasks and improves performance of specific tasks. |
| Approach: | They propose a task selection method that leverages instruction information alone to identify relevant tasks and optimize instruction tuning for specific tasks. |
| Outcome: | The proposed method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. |
Local Temperature Beam Search: Avoid Neural Text DeGeneration via Enhanced Calibration (2023.findings-acl)
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Dongkyu Lee, Gyeonghun Kim, Janghoon Han, Taesuk Hong, Yi-Reun Kim, Stanley Jungkyu Choi, Nevin L. Zhang
| Challenge: | Existing approaches to inference have been based on stochastic decoding but they sacrifice output quality due to randomness. |
| Approach: | They propose a deterministic decoding scheme, local temperature beam search, which reduces repetition while maintaining the level of coherence as in beam search. |
| Outcome: | The proposed inference scheme reduces repetition while maintaining coherence as in beam search. |
ReSQL: Self-Improving Framework for Reasoning-Aware Text-to-SQL Dataset Generation (2026.findings-acl)
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| Challenge: | Experimental results show that ReSQL significantly improves execution accuracy and self-correction ability over strong baselines. |
| Approach: | They propose a framework that generates and learns from its own error-reasoning dataset . it allows models to internalize robust error-reference patterns and apply them to unseen queries . |
| Outcome: | The proposed framework improves execution accuracy and self-correction ability over strong baselines. |