63 citations · 79 across the 19 of their papers we have counts for
6 papers · 1 filter
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models
Dan Shi, Chaobin You, Jiantao Huang +2
As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dat…
RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models
Tianhao Shen, Sun Li, Quan Tu +1
The rapid evolution of large language models necessitates effective benchmarks for evaluating their role knowledge, which is essential for establishing connections with the real wo…
Evaluating Large Language Models: A Comprehensive Survey
Zishan Guo, Renren Jin, Chuang Liu +8
Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous dow…
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
Yimin Jing, Renren Jin, Jiahao Hu +4
The effective assessment of the instruction-following ability of large language models (LLMs) is of paramount importance. A model that cannot adhere to human instructions might be…
Towards a Deep Understanding of Multilingual End-to-End Speech Translation
Haoran Sun, Xiaohu Zhao, Yikun Lei +2
In this paper, we employ Singular Value Canonical Correlation Analysis (SVCCA) to analyze representations learnt in a multilingual end-to-end speech translation model trained over…
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?
Leiyu Pan, Supryadi, Deyi Xiong
Robustness, the ability of models to maintain performance in the face of perturbations, is critical for developing reliable NLP systems. Recent studies have shown promising results…