4 citations · 15 across the 13 of their papers we have counts for
5 papers · 1 filter
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
Chen Yang, Junzhuo Li, Xinyao Niu +11
Uncovering early-stage metrics that reflect final model performance is one core principle for large-scale pretraining. The existing scaling law demonstrates the power-law correlati…
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Xingwei Qu, Yiming Liang, Yucheng Wang +10
Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process…
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
Yujie Shao, Xinrong Yao, Xingwei Qu +5
Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a lar…
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Alex Zhuang, Ge Zhang, Tianyu Zheng +7
Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain tex…
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
Yizhi LI, Ge Zhang, Xingwei Qu +16
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-fol…