Papers by Hyunsoo Lee
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. |
LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models (2025.emnlp-industry)
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| Challenge: | Creating high-quality datasets for large language models often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly. |
| Approach: | They propose a framework that operates entirely on CPUs to streamline the processes of dataset extraction, filtering, and curation. |
| Outcome: | The proposed framework reduces preparation time and costs while maintaining high data quality while enhancing the applicability of LLMs in specialized contexts. |
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. |
Inertia in Moral and Value Judgments of Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models behave non-deterministically, and prompting is a common method for steering their outputs. |
| Approach: | They use role-play at scale to study the value orientation and inertia of Large Language Models. |
| Outcome: | The proposed model keeps values skewed in one direction across persona settings. |
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 . |
Instruction Tuning with Human Curriculum (2024.findings-naacl)
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| Challenge: | a recent study shows that human curriculum-inspired strategies can enhance performance of large language models. |
| Approach: | They propose a method for generating instruction-response datasets that emulate human learning . they find that substantial improvements can be achieved through curriculum ordering . |
| Outcome: | The proposed method achieves performance improvements on truthfulQA, MMLU, OpenbookQA, and ARC-hard benchmarks without additional computational costs. |
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. |
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora (2025.acl-short)
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| Challenge: | Existing methods to efficiently filter large web corpora require GPU resources. |
| Approach: | They propose an ensemble approach that leverages two contrasting KenLMs to filter large web corpora. |
| Outcome: | The proposed method significantly reduces noisy content while preserving high-quality content compared to the traditional KenLM training method. |
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) externalize explicit reasoning traces before producing the answer, yet suffer from overthinking challenge. |
| Approach: | They propose a framework that enables large reasoning models to self-determine when to reason by training only the initial root token via group relative reward and group-wise advantages. |
| Outcome: | The proposed framework reduces training overhead and VRAM usage by focusing on the root token . it learns difficulty-aware adaptive thinking at just 2% of the training compute of prior methods. |
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. |