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Found 5 papers, 1 papers with code
Reviving Dormant Memories: Investigating Catastrophic Forgetting in Language Models through Rationale-Guidance Difficulty
In this paper, we discover that when a forgetting model passively receives an externally provided partial appropriate rationale, its performance on the forgotten task can be restored.
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as In-Context Alignment (ICA), indicating that models can comprehend human instructions without requiring parameter adjustments.
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Language style is necessary for AI systems to understand and generate diverse human language accurately.
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression
In addition, most approaches ignore the spatial and channel redundancy.
