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Found 5 papers, 2 papers with code
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Five setups were designed to assess ML and FT model performance across different data availability scenarios. Datasets for curation tasks included: task 1 (620, 386), task 2 (611, 430), and task 3 (617, 381), maintaining a 50:50 positive versus negative ratio.
Self-Consistency of Large Language Models under Ambiguity
Using this test, we find that despite increases in self-consistency, models usually place significant weight on alternative, inconsistent answers.
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
We use this improved benchmark to evaluate recent model editing techniques and find that they suffer from low specificity.
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
The most interesting words in scientific texts will often be novel or rare.
Domain-adaptation of spherical embeddings
Two new document classification data-sets are collated from general and chemistry scientific journals to compare the proposed update training strategies with benchmark models.
