Search Results for author:
Found 8 papers, 7 papers with code
AlignBench: Benchmarking Chinese Alignment of Large Language Models
We will provide public APIs for evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs' Chinese alignment.
CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation
Since the natural language processing (NLP) community started to make large language models (LLMs), such as GPT-4, act as a critic to evaluate the quality of generated texts, most of them only train a critique generation model of a specific scale on specific datasets.
Black-Box Prompt Optimization: Aligning Large Language Models without Model Training
However, these models are often not well aligned with human intents, which calls for additional treatments on them, that is, the alignment problem.
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.
PAL: Persona-Augmented Emotional Support Conversation Generation
As previous studies have demonstrated that seekers' persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support.
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.
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.
