Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

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. By contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws, and other regulations and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors, which work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company’s internal-facing enterprise chatbot to its business conduct guidelines.

Authors

Institutions

Publication Details

Journal
IEEE Internet Computing
Published
2024-09-01
DOI
https://doi.org/10.1109/mic.2024.3453671
Citations
4
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
1.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Inkit Padhi, Pierre Dognin, Kush R. Varshney, Orna Raz et al.
4 citations
IEEE Internet Computing
Topic Modeling
1.39
article

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations

Inkit Padhi, Pierre Dognin, Kush R. Varshney, Orna Raz, Djallel Bouneffouf, Prasanna Sattigeri, Eitan Farchi, Manish Nagireddy, Rosario Uceda‐Sosa, Aleksandra Mojsilović, Ioana Baldini, Joan Byamugisha, Siphiwe Thwala, Ndivhuwo Makondo, Swapnaja Achintalwar, Maria Chang, Jesus Rios, Karthikeyan NatesanRamamurthy, Moninder Singh
article en
4 citations

Abstract

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. By contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws, and other regulations and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors, which work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company’s internal-facing enterprise chatbot to its business conduct guidelines.

IEEE Internet ComputingVol. 28(5)
Google (United States) (US), IBM (United States) (US), Cambridge Scientific (United States) (US), IBM Research - India (IN), IBM Research - Thomas J. Watson Research Center (US), IBM (India) (IN), IBM Research - Africa (ZA), IBM Research - Haifa (IL)
Openalex Percentile: Top 16%
Topic Modeling
1.39
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.