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Found 6 papers, 3 papers with code
Who’s on First?: Probing the Learning and Representation Capabilities of Language Models on Deterministic Closed Domains
The capabilities of today’s natural language processing systems are typically evaluated using large datasets of curated questions and answers.
LawLLM: Law Large Language Model for the US Legal System
Moreover, the subtle distinctions between similar and precedent cases require a deep understanding of legal knowledge.
Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness
Academic literature does not give much guidance on how to build the best possible customer-facing summarization system from existing research components.
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models
In this work, we present compositional fine-tuning (CFT): an approach based on explicitly decomposing a target task into component tasks, and then fine-tuning smaller LMs on a curriculum of such component tasks.
Stolen Probability: A Structural Weakness of Neural Language Models
Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space.
Just Add Functions: A Neural-Symbolic Language Model
How can we augment today's neural models with such encodings?
