6 accepted papers
n the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose AgriEval, the first comprehensive Chinese agricultural benchmark with three main characteristics: (1) Comprehensive Capabil
Video recommendation systems heavily rely on user watch time feedback, making accurate watch time prediction a crucial task. However, this task inherently suffers from bias, as recommendation models tend to favor long-duration videos to maximize watch time. This issue, known as duration bias in the
Multi-modal Large Language Models (MLLMs) integrating images, text, and speech can provide farmers with accurate diagnoses and treatment of pests and diseases, enhancing agricultural efficiency and sustainability. However, existing benchmarks lack comprehensive evaluations, particularly in multi-lev…
Reinforcement Learning from Human Feedback (RLHF) has been shown to effectively align large language models (LLMs) with human knowledge. However, the lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain. Humans in RLHF play a critical role in inj…
The generation of logically coherent dialogues by humans relies on underlying cognitive abilities. Based on this, we redefine the dialogue coherence evaluation process, combining cognitive judgment with the basic text to achieve a more human-like evaluation. We propose a novel dialogue evaluation fr…
Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cro…