Search Results for author:
Found 6 papers, 3 papers with code
In-Context Learning State Vector with Inner and Momentum Optimization
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
Improving Attributed Text Generation of Large Language Models via Preference Learning
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context.
A Survey of Large Language Models Attribution
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
MSDF: A General Open-Domain Multi-Skill Dialog Framework
In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e. g. knowledge grounded dialog and persona based dialog).
