Papers by Youna Kim
When to Speak, When to Abstain: Contrastive Decoding with Abstention (2025.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
Copied to clipboard
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. |
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels (2023.acl-long)
Copied to clipboard
| 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. |
CUB: Benchmarking Context Utilisation Techniques for Language Models (2026.acl-long)
Copied to clipboard
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. |