3 citations · 5 across the 6 of their papers we have counts for
8 papers
Apriel-H1: Towards Efficient Enterprise Reasoning Models
Oleksiy Ostapenko, Luke Kumar, Raymond Li +10
Large Language Models (LLMs) achieve remarkable reasoning capabilities through transformer architectures with attention mechanisms. However, transformers suffer from quadratic time…
Apriel-1.5-15b-Thinker
Shruthan Radhakrishna, Aman Tiwari, Aanjaneya Shukla +21
We present Apriel-1.5-15B-Thinker, a 15-billion parameter open-weights multimodal reasoning model that achieves frontier-level performance through training design rather than sheer…
Apriel-Nemotron-15B-Thinker
Shruthan Radhakrishna, Soham Parikh, Gopal Sarda +32
While large language models (LLMs) have achieved remarkable reasoning capabilities across domains like code, math and other enterprise tasks, their significant memory and computati…
TapeAgents: a Holistic Framework for Agent Development and Optimization
Dzmitry Bahdanau, Nicolas Gontier, Gabriel Huang +10
We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents…
Exploring Zero and Few-shot Techniques for Intent Classification
Soham Parikh, Quaizar Vohra, Prashil Tumbade +1
Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers pu…
Automated Utterance Generation
Soham Parikh, Quaizar Vohra, Mitul Tiwari
Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-ans…
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples
Soham Parikh, Ananya B. Sai, Preksha Nema +1
When humans learn to perform a difficult task (say, reading comprehension (RC) over longer passages), it is typically the case that their performance improves significantly on an e…
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions
Soham Parikh, Ananya B. Sai, Preksha Nema +1
The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given passage, question pair and select one of the n given options. The cu…