Education
IDGen: ItemDiscriminationInduced PromptGenerationforLLMEvaluation
Item Discrimination (ID) theory, which is widely used in educational assessment, measures the ability of individual test items to differentiate between high and low performers. Inspired by this theory, wepropose anID-induced prompt synthesis frameworkforevaluating LLMs to ensure the evaluation set can continually update and refine according to model abilities.
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control with many control units.
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback Marcel T orne 1,2 Max Balsells 3 Zihan Wang
This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.
MedJourney: Benchmark and Evaluation of Large Language Models over Patient Clinical Journey Xian Wu
In this paper, we address this gap by dividing a typical patient's clinical journey
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
LLMs' flexibility is largely due to an "in-context learning" effect that emerges when
ACTOR S
We also demonstrate its effectiveness in generating robotic tasks.
C-E VAL: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models Y uzhen Huang 1 Y uzhuo Bai
Evaluation benchmarks are at the core role for AI development.
