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pro vyhledávání: '"Heck, Larry"'
Large language models (LLMs) are essential in natural language processing (NLP) but are costly in data collection, pre-training, fine-tuning, and inference. Task-specific small language models (SLMs) offer a cheaper alternative but lack robustness an
Externí odkaz:
http://arxiv.org/abs/2410.18287
Autor:
Punjwani, Saif, Heck, Larry
As virtual agents become increasingly prevalent in human-computer interaction, generating realistic and contextually appropriate gestures in real-time remains a significant challenge. While neural rendering techniques have made substantial progress w
Externí odkaz:
http://arxiv.org/abs/2410.16533
Autor:
Punjwani, Saif, Heck, Larry
The scarcity of high-quality, multimodal training data severely hinders the creation of lifelike avatar animations for conversational AI in virtual environments. Existing datasets often lack the intricate synchronization between speech, facial expres
Externí odkaz:
http://arxiv.org/abs/2410.16503
Retrieval augmented generation (RAG) systems augment how knowledge language models are by integrating external information sources such as Wikipedia, internal documents, scientific papers, or the open internet. RAG systems that rely on the open inter
Externí odkaz:
http://arxiv.org/abs/2408.11189
Autor:
Lizzo, Tyler, Heck, Larry
Given the prevalence of large language models (LLMs) and the prohibitive cost of training these models from scratch, dynamically forgetting specific knowledge e.g., private or proprietary, without retraining the model has become an important capabili
Externí odkaz:
http://arxiv.org/abs/2408.04140
We present Reinforcement Learning via Auxiliary Task Distillation (AuxDistill), a new method that enables reinforcement learning (RL) to perform long-horizon robot control problems by distilling behaviors from auxiliary RL tasks. AuxDistill achieves
Externí odkaz:
http://arxiv.org/abs/2406.17168
An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be develo
Externí odkaz:
http://arxiv.org/abs/2406.08398
This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations situated in tables from scientific articles. This dataset is designed to facilitate human-AI collaborative problem-solving through AI-powered multi-task tabular
Externí odkaz:
http://arxiv.org/abs/2404.12580
Distilling large, unstructured text into a structured, condensed form such as tables is an open research problem. One of the primary challenges in automatically generating tables is ensuring their syntactic validity. Prior approaches address this cha
Externí odkaz:
http://arxiv.org/abs/2403.14457
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human). We analyze three use cases of LLMs: interactions over web content, code, and mathemati
Externí odkaz:
http://arxiv.org/abs/2403.05045