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pro vyhledávání: '"Hebert, Liam"'
Autor:
Sayana, Krishna, Vasudeva, Raghavendra, Vasilevski, Yuri, Su, Kun, Hebert, Liam, Pham, Hubert, Jash, Ambarish, Sodhi, Sukhdeep
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for natural lan
Externí odkaz:
http://arxiv.org/abs/2410.16780
Autor:
Hebert, Liam, Kyriakidi, Marialena, Pham, Hubert, Sayana, Krishna, Pine, James, Sodhi, Sukhdeep, Jash, Ambarish
Hybrid recommender systems, combining item IDs and textual descriptions, offer potential for improved accuracy. However, previous work has largely focused on smaller datasets and model architectures. This paper introduces Flare (Fusing Language model
Externí odkaz:
http://arxiv.org/abs/2409.11699
Autor:
Hebert, Liam, Sayana, Krishna, Jash, Ambarish, Karatzoglou, Alexandros, Sodhi, Sukhdeep, Doddapaneni, Sumanth, Cai, Yanli, Kuzmin, Dima
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences. To address this, we introduce PERSOMA, Personalized Soft Prompt Ada
Externí odkaz:
http://arxiv.org/abs/2408.00960
The lack of fluency in sign language remains a barrier to seamless communication for hearing and speech-impaired communities. In this work, we propose a low-cost, real-time ASL-to-speech translation glove and an exhaustive training dataset of sign la
Externí odkaz:
http://arxiv.org/abs/2407.12020
Autor:
Hebert, Liam, Sahu, Gaurav, Guo, Yuxuan, Sreenivas, Nanda Kishore, Golab, Lukasz, Cohen, Robin
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech
Externí odkaz:
http://arxiv.org/abs/2307.09312
Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled
Externí odkaz:
http://arxiv.org/abs/2301.10871
We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-the-art Graph Transformer Networks to analyze all conversations that f
Externí odkaz:
http://arxiv.org/abs/2301.04248
Autor:
Hebert, Liam, Makki, Raheleh, Mishra, Shubhanshu, Saghir, Hamidreza, Kamath, Anusha, Merhav, Yuval
Publikováno v:
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). pages 83-89
Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-com
Externí odkaz:
http://arxiv.org/abs/2210.07472
A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each participating agent's model weights into one common model (FedAvg). We instea
Externí odkaz:
http://arxiv.org/abs/2205.13697
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