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XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference …
Workshop at the Neural Information Processing Systems (NeurIPS),  2024.
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Context is Key: A Benchmark for Forecasting with Essential Textual Information
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks …
Foundation Models for Time Series,  2024.
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Representing Positional Information in Generative World Models for Object Manipulation
Representing Positional Information in Generative World Models for Object Manipulation
The ability to predict outcomes of interactions between embodied agents and objects is paramount in the robotic setting. While …
Learning Effective Abstractions for Planning,  2024.
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Multimodal foundation world models for generalist embodied agents
Multimodal foundation world models for generalist embodied agents
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement …
Workshop at the International Conference of Machine Learning (ICML),  2024.
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Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
Early Exiting (EE) is a promising technique for speeding up inference at the cost of limited performance loss. It adaptively allocates …
Workshop at the International Conference of Machine Learning (ICML),  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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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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Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. …
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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