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Found 11 papers, 5 papers with code
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM
Consequently, we propose a simple and safe prompt engineering method (SSP) to improve image generation quality by providing optimal camera descriptions.
COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind.
Automated Paper Screening for Clinical Reviews Using Large Language Models
Objective: To assess the performance of the OpenAI GPT API in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review datasets and compare its performance against ground truth labelling by two independent human reviewers.
Safety Assessment of Chinese Large Language Models
To further promote the safe deployment of LLMs, we develop a Chinese LLM safety assessment benchmark.
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements
This survey presents a framework for safety research pertaining to large models, delineating the landscape of safety risks as well as safety evaluation and improvement methods.
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation
In order to detect such toxic generations, existing methods rely on templates, real-world data extraction, crowdsourcing workers, or automatic generation to construct adversarial contexts that are likely to induce toxic generations.
Perplexity from PLM Is Unreliable for Evaluating Text Quality
Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks
The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e. g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice.
COLD: A Benchmark for Chinese Offensive Language Detection
To this end, we propose a benchmark --COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset --COLDATASET and a baseline detector --COLDETECTOR which is trained on the dataset.
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings, with focuses on context-sensitive unsafety, which is under-explored in prior works.
