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Found 5 papers, 5 papers with code
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
Scientific progress depends on researchers' ability to synthesize the growing body of literature.
Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
Specifically, we find that increasing the size of the datastore used by a retrieval-based LM monotonically improves language modeling and several downstream tasks without obvious saturation, such that a smaller model augmented with a large datastore outperforms a larger LM-only model on knowledge-intensive tasks.
Challenges in Context-Aware Neural Machine Translation
Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and has given rise to a number of recent techniques.
MABEL: Attenuating Gender Bias using Textual Entailment Data
To this end, we propose MABEL (a Method for Attenuating Gender Bias using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations.
Can Rationalization Improve Robustness?
Our experiments reveal that the rationale models show the promise to improve robustness, while they struggle in certain scenarios--when the rationalizer is sensitive to positional bias or lexical choices of attack text.
