2 citations · 11 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails
Manish Nagireddy, Inkit Padhi, Soumya Ghosh +1
Large language models (LLMs) have convincing performance in a variety of downstream tasks. However, these systems are prone to generating undesirable outputs such as harmful and bi…
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…
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
Erik Miehling, Manish Nagireddy, Prasanna Sattigeri +3
Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be att…
Multi-Level Explanations for Generative Language Models
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do +8
Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain r…
SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models
Manish Nagireddy, Lamogha Chiazor, Moninder Singh +1
Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchm…
Keeping Up with the Language Models: Systematic Benchmark Extension for Bias Auditing
Ioana Baldini, Chhavi Yadav, Manish Nagireddy +2
Bias auditing of language models (LMs) has received considerable attention as LMs are becoming widespread. As such, several benchmarks for bias auditing have been proposed. At the…