Search Results for author:
Found 9 papers, 3 papers with code
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone.
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.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Hybrid Ranking Network for Text-to-SQL
In this paper, we study how to leverage pre-trained language models in Text-to-SQL.
Relation Discovery with Out-of-Relation Knowledge Base as Supervision
In this paper, we study the problem of how to use out-of-relation knowledge bases to supervise the discovery of unseen relations, where out-of-relation means that relations to discover from the text corpus and those in knowledge bases are not overlapped.
Knowledge Graph Embedding by Translating on Hyperplanes
Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling.
