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Found 7 papers, 3 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
SciAgent: Tool-augmented Language Models for Scientific Reasoning
To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning.
Language Models can be Logical Solvers
Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions.
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Hence, those models are unable to predict further future.
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities.
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species.
