Deep Learning
ShoppingMMLU: AMassiveMulti-TaskOnline ShoppingBenchmarkforLargeLanguageModels
However,existingmodelsand benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping byalleviating task-specific engineering effortsandby providing users with interactiveconversations.
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning Supplementary Material Anonymous Author(s) Affiliation Address email
Moreover, we show more visualization results in experiments. To ensure a fair comparison, we used the fusion and optimization method as same as Latefusion. When k=1, it means that the object's physical properties are only related to itself, while As described in Section 3.1 in our paper, we represent audio Table 2: Performance comparison between our proposed DSE-audio and existing baseline methods. As shown in Table 2, we compare our method with other baseline methods. In Figure 6, we show a few additional examples of clustering using dynamic factors.