6 papers
How Prompts Move Language Model Behavior: Frames, Salience, and Construal as Semantic Control
Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool +1
Prompt engineering is widely used to shape large language model behavior, yet it is often treated as a practical heuristic rather than as a form of natural-language control. This p…
-Table: A Statistical Explanation for Global SHAP
Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool +1
Global SHAP explanations are typically presented as feature-importance rankings, which identify variables that matter to a black-box model but do not indicate whether their effects…
CLAPS: Aleatoric-Epistemic Scaling via Last-Layer Laplace for Conformal Regression
Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool +1
Conformal regression provides finite-sample marginal coverage, but it does not by itself determine how interval width should adapt across heterogeneous inputs. Existing locally ada…
Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It
Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool +1
The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimiz…
SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Models
Wonjun Jeong, Dongseok Kim, Taegkeun Whangbo
Large Language Models (LLMs) can achieve inflated scores on multiple-choice tasks by exploiting inherent biases in option positions or labels, rather than demonstrating genuine und…
A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Dongseok Kim, Gisung Oh
Conventional regularization is designed to control variance, but in small-data regression it can also aggravate underfitting when predictive signal is concentrated in weak directio…