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Found 7 papers, 5 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.
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Our evaluations show that the best model in any given evaluation reaches on average 87% of ChatGPT performance, and 73% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs.
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.
Data-Efficient Finetuning Using Cross-Task Nearest Neighbors
Obtaining labeled data to train a model for a task of interest is often expensive.
Hyperdecoders: Instance-specific decoders for multi-task NLP
We investigate input-conditioned hypernetworks for multi-tasking in NLP, generating parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder.
Local Interpretations for Explainable Natural Language Processing: A Survey
As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models.
