12 papers
Granite Embedding Multilingual R2 Models
Parul Awasthy, Aashka Trivedi, Yushu Yang +14
We introduce the multilingual Granite Embedding R2 models, a family of encoder-based embedding models for enterprise-scale dense retrieval across 200+ languages. Extending our Engl…
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +24
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language…
Self-Speculative Decoding for LLM-based ASR with CTC Encoder Drafts
George Saon, Samuel Thomas, Takashi Fukuda +3
We propose self-speculative decoding for speech-aware LLMs by using the CTC encoder as a draft model to accelerate auto-regressive (AR) inference and improve ASR accuracy. Our thre…
ToolRM: Outcome Reward Models for Tool-Calling Large Language Models
Mayank Agarwal, Ibrahim Abdelaziz, Kinjal Basu +4
As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has emerged as a critical yet underexplored area of research. Existing rewar…
Activated LoRA: Fine-tuned LLMs for Intrinsics
Kristjan Greenewald, Luis Lastras, Thomas Parnell +6
Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven custom…
A Library of LLM Intrinsics for Retrieval-Augmented Generation
Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…