综合来看,合理选择Num_latent和λ参数对于优化关系嵌入的模型至关重要,可以有效地改善推荐系统的效能。 原文《Sequential Recommendation with Latent Relations based on Large Language Model》
Performance Comparison (RQ1) RLMRec,展现其普遍适用性,无论在何种基础模型上都能提升准确性和相关性。 虽然增益可能因初始条件略有不同,但总体上,RLMRec,保证了推荐的稳定性。 在应对噪声数据时,RLMRec,推荐准确度不受噪声影响。 尽管增加了复杂性,但RLMRec,证实其高效性。 RLMRec,验证了其在不同领域的适...
With many studies discussing the comparison of the capabilities of large language models, there is not much research that directly discusses the comparison of the performance of large language models in producing Indonesian cultural content. This research compares the ...
Figure 2. Performance comparison of the proposed model on the BuzzFeed dataset with and without data augmentation. Figure 3. Performance comparison of the proposed model on the PolitiFact dataset with and without data augmentation. On the BuzzFeed dataset, without data augmentation, the model achie...
track and compare your model performance visually. In addition,NeptuneandW&Bintegration can be used. chat with your model and get instant feedback on your model performance. easily export your model to theHugging Face Huband share it with the community. ...
will continue to work on improving LLMPerf (in particular to make it easier to control the distribution of inputs and outputs) in the hope that it will improve transparency and reproducibility. We also hope you will be able to use it to model cost and performance for your particular ...
Performance Comparison (3rd May 2024) Task:Devise a machine learning model to predict the survival of passengers on the Titanic. The output should include the accuracy of the model and visualizations of the confusion matrix, correlation matrix, and other relevant metrics. ...
ModelPre-training data Empty CellEnZhTotal GPT-Neo-1.3B 380B – 380B MindLLM-1.3B 241B 82B 323B Results and analysis. The results are presented in Table 5. In comparison to GPT-Neo, MindLLM-1.3B exhibited superior average performance (26.6 vs 24.1) in English tasks with much smaller tr...
model performance but also by the need to implement responsible AI and by the need to mitigate the risk of providing misinformation or biased content and to minimize the generation of harmful, unsafe, malicious and unethical content. Furthermore, evaluating LLMs can also ...
While benchmarks are solid indicators of LLM performance, they can’t predict how well a model will operate in the real world. Here are a few constraints of LLM benchmarks: Bounded scoring Once a model reaches the highest possible score for a certain benchmark, that benchmark will need to...