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Found 9 papers, 4 papers with code
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Since the release of T\"ULU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques.
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation
By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage.
Staged Training for Transformer Language Models
As an alternative, we consider a staged training setup that begins with a small model and incrementally increases the amount of compute used for training by applying a "growth operator" to increase the model depth and width.
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language.
Shallow Syntax in Deep Water
Shallow syntax provides an approximation of phrase-syntactic structure of sentences; it can be produced with high accuracy, and is computationally cheap to obtain.
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
We revisit domain adaptation for parsers in the neural era.
Construction of the Literature Graph in Semantic Scholar
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
AllenNLP: A Deep Semantic Natural Language Processing Platform
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers.
