Papers by Minjin Jeon
Rectifying Demonstration Shortcut in In-Context Learning (2024.naacl-long)
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| Challenge: | Large language models (LLMs) can solve tasks with a few demonstrations, but often rely on their pre-trained semantic priors rather than the input-label relationships to proceed with ICL prediction. |
| Approach: | They propose a demonstration-aware calibration method to improve LLMs' ability to learn new input-label relationships from demonstrations. |
| Outcome: | The proposed method improves the original ICL task and the task learning setting, and the results are generalized across three LLM families. |
StepER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models (2025.emnlp-main)
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| Challenge: | Existing knowledge distillation methods overlook the need for different reasoning abilities at different steps, hindering transfer in multi-step retrieval-augmented frameworks. |
| Approach: | They propose a method that uses step-wise supervision to align with evolving information and reasoning demands across stages. |
| Outcome: | The proposed method outperforms previous methods on multi-hop QA benchmarks with an 8B model achieving performance comparable to a 70B teacher model. |