3 papers
cs.CL2026
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Wei-Lin Chen, Liqian Peng, Tian Tan +5
Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that…
cs.CL2026
You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
Wenchong He, Liqian Peng, Zhe Jiang +1
Large language models (LLMs) possess a remarkable ability to perform in-context learning (ICL), which enables them to handle multiple downstream tasks simultaneously without requir…
cs.LG2025
Compressing Many-Shots in In-Context Learning
Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta +9
Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Conte…