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InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Instruction tuning effectively optimizes Large Language Models (LLMs) for downstream tasks.
LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes
In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent.
A Thorough Examination of Decoding Methods in the Era of LLMs
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers.
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks.
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
Although instruction tuning has proven to be a data-efficient method for transforming LLMs into such generalist models, their performance still lags behind specialist models trained exclusively for specific tasks.
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
The standard definition generation task requires to automatically produce mono-lingual definitions (e. g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners.
