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Found 7 papers, 4 papers with code
How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments
Decision-making, a complicated task requiring various types of abilities, presents an excellent framework for assessing Large Language Models (LLMs).
New Job, New Gender? Measuring the Social Bias in Image Generation Models
BiasPainter uses a diverse range of seed images of individuals and prompts the image generation models to edit these images using gender, race, and age-neutral queries.
The Earth is Flat? Unveiling Factual Errors in Large Language Models
Current methods for evaluating LLMs' veracity are limited by test data leakage or the need for extensive human labor, hindering efficient and accurate error detection.
A & B == B & A: Triggering Logical Reasoning Failures in Large Language Models
In addition, the test cases of LogicAsker can be further used to design demonstration examples for in-context learning, which effectively improves the logical reasoning ability of LLMs, e. g., 10\% for GPT-4.
All Languages Matter: On the Multilingual Safety of Large Language Models
In this work, we build the first multilingual safety benchmark for LLMs, XSafety, in response to the global deployment of LLMs in practice.
Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench
Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education.
GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers.
