Search Results for author:
Found 5 papers, 0 papers with code
Automatic Prompt Selection for Large Language Models
Our approach consists of three steps: (1) clustering the training data and generating candidate prompts for each cluster using an LLM-based prompt generator; (2) synthesizing a dataset of input-prompt-output tuples for training a prompt evaluator to rank the prompts based on their relevance to the input; (3) using the prompt evaluator to select the best prompt for a new input at test time.
Universal Graph Continual Learning
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution.
When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dust
Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia.
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous.
Robust Deep Reinforcement Learning for Extractive Legal Summarization
Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles.
