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Found 6 papers, 4 papers with code
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems
We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains.
Concept-aware Training Improves In-context Learning Ability of Language Models
Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task described in a natural language input.
A Whisper transformer for audio captioning trained with synthetic captions and transfer learning
The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniques.
Resources and Few-shot Learners for In-context Learning in Slavic Languages
Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English.
Can In-context Learners Learn a Reasoning Concept from Demonstrations?
We find that most of the recent in-context learners can not consistently benefit from the demonstrated concepts, irrespective of the model size.
Soft Alignment Objectives for Robust Adaptation of Language Generation
Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application.
