Mar 7, 2024 · DEEP-ICL explicitly extracts task definitions from given demonstrations and generates responses through learning task-specific examples. We argue that improvement from ICL does not directly rely on model size, but essentially stems from understanding task definitions and task-guided learning.

DEEP-ICL explicitly extracts task definitions from given demonstrations and generates responses through learning task-specific examples. We argue that improvement from ICL does not directly rely on model size, but essentially stems from understanding task …

ICL. DEEP-ICL explicitly extracts task defi-nitions from given demonstrations and gener-ates responses through learning task-specific examples. We argue that improvement from ICL does not directly rely on model size, but essentially stems from understanding task def-initions and task-guided learning. Inspired by this, DEEP-ICL combines two 3B ...

DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning X Qu, Y Liang, Y Wang, T Zheng, T Yue, L Ma, SW Huang, J Zhang, ... arXiv e-prints, arXiv: 2403.04233 , 2024

Mar 7, 2024 · To address these questions, we propose \textbf {TEGEE} (Task Definition Guided Expert Ensembling), a method that explicitly extracts task definitions and generates responses based on specific tasks.

Dec 16, 2024 · DEEP-ICL represents a promising approach for improving the few-shot learning capabilities of large language models by incorporating specialized knowledge from definition-enriched experts.

Figure 1: Pipeline of DEEP-ICL. The preparation stage focuses on generating a pool of task-based experts, each trained using task-specific data. ICL firstly generates task definitions from given demonstrations using a task definition generator, which can be a …

Our proposed model, DeepICL (Deep Interaction-Conditioned Ligand generative model), employs an interaction condition that captures the local pocket environment to precisely control the generation process of a ligand inside a binding pocket.

Nov 6, 2024 · The training objective in the paper is designed to train a transformer model to perform in-context learning (ICL). In ICL, the model is trained to learn how to predict outputs for new inputs based on a sequence of input-output pairs provided in the context, without updating its parameters. The following is the training objective.

Aug 13, 2024 · Qu et al.’s DEEP-ICL (Definition-Enriched Experts for Language Model In-Context Learning) method effectively improves ICL performance through five stages: expert pool construction, task definition extraction, guided retrieval, …