13 citations · 33 across the 27 of their papers we have counts for
6 papers · 1 filter
Data Management For Training Large Language Models: A Survey
Zige Wang, Wanjun Zhong, Yufei Wang +6
Data plays a fundamental role in training Large Language Models (LLMs). Efficient data management, particularly in formulating a well-suited training dataset, is significant for en…
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Boyang Xue, Weichao Wang, Hongru Wang +7
Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
Yuxin Jiang, Yufei Wang, Xingshan Zeng +7
The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure res…
M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models
Wai-Chung Kwan, Xingshan Zeng, Yufei Wang +5
Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systemat…
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
Kai Chen, Chunwei Wang, Kuo Yang +11
The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when…
SELF: Self-Evolution with Language Feedback
Jianqiao Lu, Wanjun Zhong, Wenyong Huang +9
Large Language Models (LLMs) have demonstrated remarkable versatility across various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution with Language Feedback), a…