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Found 5 papers, 1 papers with code
LLMs Instruct LLMs:An Extraction and Editing Method
The interest in updating Large Language Models (LLMs) without retraining from scratch is substantial, yet it comes with some challenges. This is especially true for situations demanding complex reasoning with limited samples, a scenario we refer to as the Paucity-Constrained Complex Reasoning Adaptation for LLMs (PCRA-LLM). Traditional methods like Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation (RAG) are inadequate for this critical issue, particularly evident in our exploration of a specific medical context that epitomize the PCRA-LLM's distinct needs. To address the issue, we propose a Sequential Fusion method to incorporate knowledge from complex context into LLMs.
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency
The intra-client and inter-client consistency learning are introduced to smooth predictions at the data level and avoid confirmation bias of local models.
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning.
Is it Possible to Edit Large Language Models Robustly?
Q3: Which knowledge features are correlated with the performance and robustness of editing?
