Papers by Haeun Yu
DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models (2024.findings-emnlp)
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| Challenge: | LMs are useful in a variety of downstream applications from summarization to fact-checking, often relying on factual knowledge memorized during pre-training. |
| Approach: | They use two knowledge conflict measures and a novel dataset DYNAMICQA to examine the effect of intra-memory conflict on LMs' ability to accept contextual knowledge. |
| Outcome: | The proposed model can accept contextual knowledge with a higher degree of accuracy than models with fewer truth values. |
A Reality Check on Context Utilisation for Retrieval-Augmented Generation (2025.acl-long)
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Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, Arnav Arora, Christina Lioma, Maria Maistro, Pepa Atanasova, Isabelle Augenstein
| Challenge: | Existing studies on LM context utilisation of retrieved information have focused on synthetic text. |
| Approach: | They propose a dataset of unreliable, insufficient and difficult-to-understand contexts with real-world queries and contexts manually annotated for stance to compare them to synthetic datasets. |
| Outcome: | The proposed model outperforms synthetic datasets and exaggerates rare context characteristics, leading to inflated context utilisation results. |
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Existing language models (LMs) can be distracted by irrelevant contexts or ignore relevant information that contradicts outdated parametric memory.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. |
Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods (2024.acl-long)
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| Challenge: | Language Models acquire parametric knowledge from their training process, embedding it within their weights. |
| Approach: | They propose a new evaluation framework to quantify and compare the knowledge revealed by Instance Attribution and Neuron Attributions. |
| Outcome: | The proposed evaluation framework compares the knowledge revealed by IA and NA with that of neuron attribution methods. |