Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Golab, Lukasz"'
Expert search and team formation systems operate on collaboration networks, with nodes representing individuals, labeled with their skills, and edges denoting collaboration relationships. Given a keyword query corresponding to the desired skills, the
Externí odkaz:
http://arxiv.org/abs/2405.12881
This paper demonstrates RAGE, an interactive tool for explaining Large Language Models (LLMs) augmented with retrieval capabilities; i.e., able to query external sources and pull relevant information into their input context. Our explanations are cou
Externí odkaz:
http://arxiv.org/abs/2405.13000
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
Autor:
Rorseth, Joel, Godfrey, Parke, Golab, Lukasz, Kargar, Mehdi, Srivastava, Divesh, Szlichta, Jaroslaw
Towards better explainability in the field of information retrieval, we present CREDENCE, an interactive tool capable of generating counterfactual explanations for document rankers. Embracing the unique properties of the ranking problem, we present c
Externí odkaz:
http://arxiv.org/abs/2302.04983
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
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
We propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision. We start with an iterative framework in which an input sentence is revised using explicit edit operations, and add paraphrasing as a
Externí odkaz:
http://arxiv.org/abs/2203.09742
Publikováno v:
In Information Systems January 2025 127