Papers by Sang-goo Lee
When to Speak, When to Abstain: Contrastive Decoding with Abstention (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging pre-trained (parametric) and external (contextual) knowledge. |
| Approach: | They propose a training-free decoding method that allows LLMs to generate responses when relevant knowledge is available and to abstain otherwise. |
| Outcome: | The proposed method can generate responses when relevant knowledge is available and abstain otherwise. |
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts (2024.findings-emnlp)
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Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Recent research has been developed to amplify contextual knowledge over parametric knowledge of large language models (LLMs) in knowledge-intensive tasks such as open-domain question-answering . |
| Approach: | They propose to amplify contextual knowledge over parametric knowledge of large language models (LLMs) by contrastive decoding to leverage contextual influence effectively. |
| Outcome: | The proposed approach improves open-domain question answering tasks especially in robustness by remaining undistracted by noisy contexts in retrieval-augmented generation. |
Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods for extracting complete (binary) parses from pre-trained language models are expensive and time-consuming. |
| Approach: | They propose a chart-based method and an effective top-K ensemble technique to extractbinary parses from PLMs. |
| Outcome: | The proposed method can induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, and is robust to cross-lingual transfer. |
Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
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Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Large language models (LLMs) are not specifically trained to deal with ambiguous utterances . ambiguity can lead to varying interpretations of the same input based on different assumptions or background knowledge . |
| Approach: | They propose a pipeline that aligns large language models to manage ambiguous queries . they propose to use their own assessment of perceived ambiguity to detect and manage queries a . |
| Outcome: | Experimental results show that APA empowers LLMs to detect and manage ambiguous queries while retaining the ability to answer clear questions. |
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching (P19-1)
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| Challenge: | Existing approaches to text matching consider each sequence separately . a proposed model uses both sequences to generate a given relationship with a source sequence . |
| Approach: | They propose a latent variable model for predicting the relationship between a pair of text sequences by generating a sequence that has a given relationship with a source sequence. |
| Outcome: | The proposed model achieves state-of-the-art on natural language inference and paraphrase identification. |
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels (2023.acl-long)
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| Challenge: | Utilizing language models without internal access is becoming an attractive paradigm in the field of NLP . prompting has shown progressive performance enhancements in situations where data labels are scarce or unavailable. |
| Approach: | They propose a method that uses a weak-supervision signal to train a lightweight model without internal access to data labels. |
| Outcome: | The proposed method improves text classification accuracy with weak-supervision signal without accessing weights or gradients of the LM model or data labels. |
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 . |
Think Just Enough: Leveraging Self-Assessed Confidence for Adaptive Reasoning in Language Models (2026.findings-eacl)
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| Challenge: | Recent advances in large reasoning models (LLMs) have shown remarkable capabilities in complex tasks such as mathematical problem solving and code generation. |
| Approach: | They propose a method for optimizing reasoning length via self-assessed confidence. |
| Outcome: | The proposed method improves computational efficiency without compromising answer quality. |
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (2022.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. |
| Approach: | They propose a framework that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. |
| Outcome: | The proposed framework is significantly more effective than previous studies in intent classification and OOD datasets. |
Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have been utilized in various studies, but their training sequences and text labels can alter their pre-trained weights, reducing their ability to construct and comprehend natural language sentences. |
| Approach: | They propose a reconstruction-based LLM recommendation model that harnesses the feature extraction capability of LLMs while preserving LLM’s sentence generation abilities. |
| Outcome: | The proposed model exploits the key features of both user and item pseudo-labels generated from user reviews while training on sequential data. |
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| Challenge: | Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores. |
| Approach: | They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary. |
| Outcome: | The proposed model improves on CNN/Daily Mail and New York Times datasets. |
Self-Guided Contrastive Learning for BERT Sentence Representations (2021.acl-long)
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| Challenge: | Existing methods to derive sentence embeddings from pre-trained Transformers are unclear . a self-guided training method is used to fine-tune BERT in a supervised fashion . |
| Approach: | They propose a contrastive learning method that utilizes self-guidance to improve BERT sentence representations. |
| Outcome: | The proposed method is more effective than baselines on diverse sentence-related tasks and robust to domain shifts. |
Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja (D19-1)
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| Challenge: | Existing knowledge of Korean and Chinese is based on cultural and historical reasons. |
| Approach: | They propose a method for improving Korean word representations using additional linguistic annotation by leveraging the fact that Hanja is closely related to Chinese. |
| Outcome: | The proposed approach improves representations on a novel Korean news headline generation task. |
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning (2023.starsem-1)
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| Challenge: | Pre-trained language models (PLMs) are gaining popularity on many benchmarks, but it is uncertain whether they can handle inputs that have been distributionally shifted. |
| Approach: | They evaluated various PETL techniques to detect out-of-distribution changes as the size of the PLM grows or the transfer methods are altered. |
| Outcome: | The proposed methods can detect out-of-distribution changes as the size of the PLM grows or the transfer methods are altered. |
CUB: Benchmarking Context Utilisation Techniques for Language Models (2026.acl-long)
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Lovisa Hagström, Youna Kim, Haeun Yu, Sang-goo Lee, Richard Johansson, Hyunsoo Cho, Isabelle Augenstein
| Challenge: | Existing language models (LMs) can be distracted by irrelevant contexts or ignore relevant information that contradicts outdated parametric memory. |
| Approach: | They develop a benchmark to help diagnose CMTs under diverse noisy context conditions within retrieval-augmented generation (RAG) they find that most existing CMT struggle to handle the full spectrum of context types encountered in real-world RAG scenarios. |
| Outcome: | The proposed benchmark compares seven state-of-the-art methods across three datasets and tasks, and shows that many lack the robustness needed to handle the full spectrum of context types encountered in real-world RAG scenarios. |
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. |
Task-aware Block Pruning with Output Distribution Signals for Large Language Models (2026.findings-eacl)
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| Challenge: | Existing methods to estimate block importance rely on representation similarity or computationally expensive sensitivity analyses to estimate task-aware model behavior. |
| Approach: | They propose a novel approach that quantifies block-level uncertainty from the statistics of each block’s early-exited output distribution on a calibration dataset. |
| Outcome: | Experiments show that the proposed approach preserves downstream task performance while reducing inference latency and computational cost. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
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| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
| Outcome: | The proposed model outperforms previous strong baselines on dialog response generation and user simulation tasks. |