ServiceNow AI Research

Large Language Models

PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their …
nternational ACM SIGIR Conference on Research and Development in Information Retrieval,  2024.
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EquiAdapt: Equivariant Adaptation of Large Pretrained Models
EquiAdapt: Equivariant Adaptation of Large Pretrained Models
Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to …
Workshop at the Computer Vision and Pattern Recognition Conference (CVPR),  2024.
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Reducing hallucination in structured outputs via Retrieval-Augmented Generation
Reducing hallucination in structured outputs via Retrieval-Augmented Generation
A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While large language models (LLM) have …
North American Chapter of the Association for Computational Linguistics (NAACL),  2024.
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An Empirical Exploration of Trust Dynamics in LLM Supply Chains
An Empirical Exploration of Trust Dynamics in LLM Supply Chains
With the widespread proliferation of AI systems, trust in AI is an important and timely topic to navigate. Researchers so far have …
Conference on Human Factors in Computing Systems (ACM-CHI),  2024.
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IntentGPT: Few-shot Intent Discovery with Large Language Models
IntentGPT: Few-shot Intent Discovery with Large Language Models
In today’s digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to …
Workshop at the International Conference of Learning Representation (ICLR),  2024.
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Self-evaluation and self-prompting to improve the reliability of LLMs
Self-evaluation and self-prompting to improve the reliability of LLMs
In order to safely deploy Large Language Models (LLMs), they must be capable of dynamically adapting their behavior based on their …
Workshop at the International Conference of Learning Representation (ICLR),  2024.
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WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on …
Workshop at the International Conference of Learning Representation (ICLR),  2024.
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Towards Disentangled High-level Causal Explanations in Text
In this work, we propose a causal representation learning framework for learning disentangled and intervenable high-level explanations …
Mid-Atlantic Student Colloquium on Speech, Language and Learning,  2024.
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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering
Multimodal multihop question answering is a complex task that requires reasoning over multiple sources of information, such as images …
ArXiv,  2024.
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InCoRo: In-Context Learning for Robotics Control with Feedback Loops
InCoRo: In-Context Learning for Robotics Control with Feedback Loops
One of the challenges in robotics is to enable robotic units with the reasoning capability that would be robust enough to execute …
ArXiv,  2024.
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