Search Results for author:
Found 8 papers, 6 papers with code
Large Language Model Safety: A Holistic Survey
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation.
Self-Pluralising Culture Alignment for Large Language Models
In this paper, we propose CultureSPA, a Self-Pluralising Culture Alignment framework that allows LLMs to simultaneously align to pluralistic cultures.
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
These help us curate CMoralEval that encompasses both explicit moral scenarios (14, 964 instances) and moral dilemma scenarios (15, 424 instances), each with instances from different data sources.
LFED: A Literary Fiction Evaluation Dataset for Large Language Models
The rapid evolution of large language models (LLMs) has ushered in the need for comprehensive assessments of their performance across various dimensions.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
Identifying Multiple Personalities in Large Language Models with External Evaluation
In this paper, we investigate LLM personalities using an alternate personality measurement method, which we refer to as the external evaluation method, where instead of prompting LLMs with multiple-choice questions in the Likert scale, we evaluate LLMs' personalities by analyzing their responses toward open-ended situational questions using an external machine learning model.
Evaluating Large Language Models: A Comprehensive Survey
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs.
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Comprehensively evaluating the capability of large language models in multiple tasks is of great importance.
