15 citations · 46 across the 18 of their papers we have counts for
10 papers · 1 filter
AI Steerability 360: A Toolkit for Steering Large Language Models
Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran +10
The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modifi…
Sparsity May Be All You Need: Sparse Random Parameter Adaptation
Jesus Rios, Pierre Dognin, Ronny Luss +1
Full fine-tuning of large language models for alignment and task adaptation has become prohibitively expensive as models have grown in size. Parameter-Efficient Fine-Tuning (PEFT)…
Granite Guardian
Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia +20
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with…
Evaluating the Prompt Steerability of Large Language Models
Erik Miehling, Michael Desmond, Karthikeyan Natesan Ramamurthy +5
Building pluralistic AI requires designing models that are able to be shaped to represent a wide range of value systems and cultures. Achieving this requires first being able to ev…
Value Alignment from Unstructured Text
Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri +3
Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on th…
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf +16
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In co…
Knowledge Graph Generation From Text
Igor Melnyk, Pierre Dognin, Payel Das
In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes a…
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Pierre L. Dognin, Inkit Padhi, Igor Melnyk +1
Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In thi…
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
Pierre L. Dognin, Igor Melnyk, Inkit Padhi +2
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak s…
Learning Implicit Text Generation via Feature Matching
Inkit Padhi, Pierre Dognin, Ke Bai +4
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural netwo…