Papers by Ching-Yun Ko
STAR: Spectral Truncation and Rescale for Model Merging (2025.naacl-short)
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| Challenge: | Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning. |
| Approach: | They propose a model merging technique that aims at mitigating "merging conflicts" by truncating small components in the respective spectral spaces and then an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix. |
| Outcome: | The proposed model outperforms baseline models on flan-T5 by 4.2% and is robust to hyperparamater choice. |
ImReasoner: Improving Memory-based Language Models for Reasoning-in-a-Haystack Tasks (2026.acl-long)
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Ching-Yun Ko, Payel Das, Sihui Dai, Georgios Kollias, Subhajit Chaudhury, Aurelie C. Lozano, Pin-Yu Chen
| Challenge: | despite advances, large language models exhibit brittleness on tasks that require multi-step reasoning over long contexts. |
| Approach: | They propose to explicitly encode contexts as ordered memory and perform iterative retrieval to construct reasoning chains. |
| Outcome: | The proposed frameworks fail to show emergent reasoning generalization in a weakly supervised scenario . the proposed framework is based on a synthetic benchmark to stress-test the models . |
Attention Tracker: Detecting Prompt Injection Attacks in LLMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are vulnerable to prompt injection attacks, where malicious inputs manipulate the model into ignoring original instructions and executing designated actions. |
| Approach: | They propose a training-free method that tracks attention patterns on instruction to detect prompt injection attacks without additional inference. |
| Outcome: | The proposed method shows an improvement of up to 10.0% over existing methods and performs well even on small LLMs. |
AI Steerability 360: A Toolkit for Steering Large Language Models (2026.acl-demo)
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Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran, Ching-Yun Ko, Pierre Dognin, Moninder Singh, Tejaswini Pedapati, Avinash Balakrishnan, Matthew Riemer, Dennis Wei, Inge Vejsbjerg, Elizabeth M. Daly, Kush R. Varshney
| Challenge: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Approach: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Outcome: | The toolkit is available under an Apache 2.0 license and is available on https://github.com/IBM/AISteer360. |