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cs.CL2024
FunctionChat-Bench: Comprehensive Evaluation of Language Models' Generative Capabilities in Korean Tool-use Dialogs
Shinbok Lee, Gaeun Seo, Daniel Lee +3
This study investigates language models' generative capabilities in tool-use dialogs. We categorize the models' outputs in tool-use dialogs into four distinct types: Tool Call, Ans…
cs.LG2024
Investigating Sensitive Directions in GPT-2: An Improved Baseline and Comparative Analysis of SAEs
Daniel J. Lee, Stefan Heimersheim
Sensitive directions experiments attempt to understand the computational features of Language Models (LMs) by measuring how much the next token prediction probabilities change by p…
cs.HC2024
Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs
Yifan Wang, David Stevens, Pranay Shah +10
The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, lab…