Papers by Sang-Woo Lee
Plug-and-Play Adaptation for Continuously-updated QA (2022.findings-acl)
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| Challenge: | Existing tasks to assess LMs’ efficacy as KBs do not adequately consider multiple large-scale updates. |
| Approach: | They propose a task where multiple large-scale updates are made to language models and plug-in modules are used to handle the updates. |
| Outcome: | The proposed method outperforms existing methods on zsRE QA and NQ datasets and is 4x more effective in terms of updates/forgets ratio compared to a fine-tuning baseline. |
Continuous Decomposition of Granularity for Neural Paraphrase Generation (2022.coling-1)
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| Challenge: | Prior work has shown that decomposing sentences at different levels of granularity has improved paragraph generation. |
| Approach: | They propose a model for continuous decomposing granularity for neural paraphrase generation that incorporates granules into attention. |
| Outcome: | The proposed model outperforms baseline models on Quora question pairs and Twitter URLs on two benchmarks. |
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022.emnlp-main)
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Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Taeuk Kim
| Challenge: | Intuitively, ground-truth labels should have as much impact in in-context learning as supervised learning, but the impact of the quality of demonstrations remains elusive. |
| Approach: | They propose to measure input-label correspondence and ground-truth label effect ratio . they propose to use verbosity of prompt templates and language model size as controlling factors . |
| Outcome: | The proposed metrics show that ground-truth labels have less impact than previously thought . the authors identify key components as controlling factors to achieve noise-resilient ICL . |
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung
| Challenge: | GPT-3 has been used to train large-scale language models on hundreds of billion scale data. |
| Approach: | They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models. |
| Outcome: | The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean. |
Query-Efficient Black-Box Red Teaming via Bayesian Optimization (2023.acl-long)
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| Challenge: | Existing methods for generating test cases and querying fail to be query-efficient . generative models can be used for open-domain dialogue, prompt continuation, text-to-image generation . |
| Approach: | They propose a query-efficient method that iteratively finds diverse positive test cases leading to model failures by utilizing user input and past evaluations. |
| Outcome: | The proposed method finds a significantly larger number of diverse positive test cases under limited query budget than baseline methods. |
Efficient Dialogue State Tracking by Selectively Overwriting Memory (2020.acl-main)
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| Challenge: | Recent work in dialogue state tracking (DST) is inefficient in that it predicts dialogue state at every turn from scratch. |
| Approach: | They propose a method that selectively overwrites memory for dialogue state tracking by predicting dialogue state on each memory slot and overwriting it with new values. |
| Outcome: | The proposed model achieves state-of-the-art joint goal accuracy with 51.72% in MultiWOZ 2.0 and 53.01% in MultiWoz 2.1 in an open vocabulary-based DST setting. |
How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts? (2025.findings-emnlp)
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| Challenge: | Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning . |
| Approach: | They investigate how well models identify and recover from unhelpful thoughts . they show that models are effective at identifying most unhelpfully thoughts - but struggle to recover from the same thoughts when they are injected into their thinking process . |
| Outcome: | The proposed model can identify unhelpful thoughts but struggle to recover from them . larger models struggle more than smaller models to recover short irrelevant thoughts . |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
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Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model (2022.naacl-main)
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Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, Nako Sung
| Challenge: | Recent studies on large-scale in-context language models have reported successful in-const zero- and few-shot learning ability. |
| Approach: | They investigate the effects of the pretraining corpus on in-context learning in a Korean-centric model. |
| Outcome: | The study shows that pretraining corpus size does not determine in-context learning ability . the findings suggest that in-constext learning is not always competitive . |
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)
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| Challenge: | Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging. |
| Approach: | They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention. |
| Outcome: | The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work. |
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation (2021.acl-long)
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| Challenge: | NeuralWOZ generates dialogues from user’s goal instructions and system’s API call results. |
| Approach: | They propose a framework that uses model-based dialogue simulation to generate dialogues from user’s goal instructions and system’s API call results. |
| Outcome: | The proposed framework achieves 4.4% point joint goal accuracy on average across domains and 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset. |
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)
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| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
| Outcome: | The proposed method outperforms existing methods on diverse classification tasks. |
Keep Me Updated! Memory Management in Long-term Conversations (2022.findings-emnlp)
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Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee, Sungdong Kim, Yuin Jeong, Hyeri Kim, Sang-Woo Lee, Woomyoung Park, Nako Sung
| Challenge: | Existing studies do not deal with cases where memorized information is outdated, which may cause confusion in later conversations. |
| Approach: | They propose a task where bots keep track of and bring up the latest information about users while conversing through multiple sessions. |
| Outcome: | The proposed method outperforms baselines that leave the stored memory unchanged in terms of engagingness and humanness, and a larger performance gap in the later sessions. |
Two-Step Question Retrieval for Open-Domain QA (2022.findings-acl)
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| Challenge: | Existing question retrieval models have shown a significant increase in inference speed but at the cost of lower QA performance compared to the retriever-reader pipeline. |
| Approach: | They propose a two-step question retrieval model with distant supervision to improve inference speed. |
| Outcome: | The proposed model significantly increases the performance of existing question retrieval models with a negligible loss on inference speed. |
Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP (2023.findings-emnlp)
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Hyuhng Kim, Hyunsoo Cho, Sang-Woo Lee, Junyeob Kim, Choonghyun Park, Sang-goo Lee, Kang Yoo, Taeuk Kim
| Challenge: | Despite advances in computer vision, its application on language input still needs to be explored despite its feasibility. |
| Approach: | They propose a universal domain adaptation (uniDA) benchmark for natural language that offers thorough viewpoints of the model’s generalizability and robustness. |
| Outcome: | The proposed model can handle spoken language in the real world while also detecting unprocessable inputs from the target domain. |
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method (2022.coling-1)
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| Challenge: | Recent models rely on pretrained language models that use metadata as inputs . however, these methods are either nontrivial or cost-ineffective . |
| Approach: | They propose a benchmark for evaluating attribute injection models using eight datasets . they extend adapters to include attributes independently of or jointly with the text . |
| Outcome: | The proposed method outperforms previous methods and achieves state-of-the-art performance on all datasets. |
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
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| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
| Approach: | They propose a framework for imposing roles on open-domain dialogue systems . they use few-shot learning to build a Korean dialogue dataset from scratch . |
| Outcome: | The proposed framework meets role specifications while maintaining conversational abilities. |