Search Results for author:
Found 7 papers, 4 papers with code
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits.
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content.
Making Large Language Models Better Reasoners with Alignment
To address this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm, which involves three steps: 1) fine-tuning LLMs with COT training data; 2) generating multiple COT responses for each question, and categorizing them into positive and negative ones based on whether they achieve the correct answer; 3) calibrating the scores of positive and negative responses given by LLMs with a novel constraint alignment loss.
Preference Ranking Optimization for Human Alignment
In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses.
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
(2) How can we enhance LLMs' ability to utilize tools?
A Unified Framework for Multi-intent Spoken Language Understanding with prompting
Multi-intent Spoken Language Understanding has great potential for widespread implementation.
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
To this end, we contribute to advance the study of the proactive dialogue policy to a more natural and challenging setting, i. e., interacting dynamically with users.
