3 citations · 6 across the 5 of their papers we have counts for
5 papers
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
Sirui Liang, Bohan Yu, Peiyu Wang +8
Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge. Existing benchmarks still rely on s…
Generative Calibration for In-context Learning
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu +2
As one of the most exciting features of large language models (LLMs), in-context learning is a mixed blessing. While it allows users to fast-prototype a task solver with only a few…
MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue
Pengfei Zhang, Xiaohui Hu, Kaidong Yu +4
Automatic open-domain dialogue evaluation is a crucial component of dialogue systems. Recently, learning-based evaluation metrics have achieved state-of-the-art performance in open…
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Cao Liu, Kang Liu, Shizhu He +2
We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed…
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots
Cao Liu, Kang Liu, Shizhu He +2
Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene. In this paper, we strive toward a novel task of Response Generation on Multi…