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Found 5 papers, 3 papers with code
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI).
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time.
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Our analyses reveal a significant drop in performance when testing neural models on out-of-domain data and non-English languages that may be mitigated using diverse training data.
Evaluating Deception Detection Model Robustness To Linguistic Variation
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs.
