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Found 3 papers, 1 papers with code
ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models
To address these problems, we propose \textbf{ChineseEcomQA}, a scalable question-answering benchmark focused on fundamental e-commerce concepts.
Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Models
Fine-tuning large language models (LLMs) based on human preferences, commonly achieved through reinforcement learning from human feedback (RLHF), has been effective in improving their performance.
Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment
Our method improves the success rate on adversarial samples by 10\% compared to the sample-wise approach, and achieves a 1. 3\% improvement on evaluation benchmarks such as MMLU, GSM8K, HumanEval, etc.
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