Papers by Haeun Cho
Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework (2026.findings-acl)
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| Challenge: | Multi-agent systems utilizing large language models assign authoritative roles to improve performance, yet the impact of authority bias on agent interactions remains underexplored. |
| Approach: | They propose to classify authoritative roles into legitimate, referent, and expert types and analyze their influence across 12-turn conversations using French and Raven’s power-based theory. |
| Outcome: | The proposed model enables agents to perform better in multi-agent evaluations. |
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Existing language models (LMs) can be distracted by irrelevant contexts or ignore relevant information that contradicts outdated parametric memory.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. |