Search Results for author:
Found 6 papers, 4 papers with code
CARE: Aligning Language Models for Regional Cultural Awareness
Existing language models (LMs) often exhibit a Western-centric bias and struggle to represent diverse cultural knowledge.
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
Utilizing this key insight, we propose a family of Structured Sparse Fine-Tuning (S$^{2}$FT) methods for LLMs, which concurrently achieve state-of-the-art fine-tuning performance, training efficiency, and inference scalability.
LLMBox: A Comprehensive Library for Large Language Models
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs.
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
Alignment with human preference is a desired property of large language models (LLMs).
Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation
From an evaluation perspective, we build a benchmark to judge ancient Chinese translation quality in different scenarios and evaluate the ancient Chinese translation capacities of various existing models.
