Customer-obsessed science
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June 13, 2024The fight against hallucination in retrieval-augmented-generation models starts with a method for accurately assessing it.
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June 13, 2024As in other areas of AI, generative models and foundation models — such as vision-language models — are a hot topic.
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June 07, 2024Although work involving large language models predominates, classical and more-general techniques remain well represented.
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June 16 - 21, 2024
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June 17 - 21, 2024
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July 14 - 18, 2024
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February 15, 2024
In addition to its practical implications, recent work on “meaning representations” could shed light on some old philosophical questions.
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April 16, 2024First model to work across a wide range of products uses a second U-Net encoder to capture fine-grained product details.
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March 18, 2024Tokenizing time series data and treating it like a language enables a model whose zero-shot performance matches or exceeds that of purpose-built models.
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February 20, 2024Generative AI supports the creation, at scale, of complex, realistic driving scenarios that can be directed to specific locations and environments.
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January 17, 2024Representing facts using knowledge triplets rather than natural language enables finer-grained judgments.
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ACL 20242024The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks
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ACL 20242024Retrieval is a widely adopted approach for improving language models leveraging external information. As the field moves towards multimodal large language models, it is important to extend the pure text-based methods to incorporate other modalities in retrieval as well for applications across the wide spectrum of machine learning tasks and data types. In this work, we propose multi-modal retrieval with
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ACL 20242024Current knowledge-editing approaches struggle to effectively propagate updates to interconnected facts. In this work, we delve into the barriers that hinder the appropriate propagation of updated knowledge within these models for accurate reasoning. To support our analysis, we introduce a novel reasoning-based benchmark, ReCoE (Reasoning-Based Counterfactual Editing dataset), which covers six common reasoning
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Exploring ordinality in text classification: A comparative study of explicit and implicit techniquesACL 20242024Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that explicitly account for the ordinal nature of labels. However, with the advent of Pretrained Language
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2024In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts. This approach aims to reduce redundant memory IO costs, a significant factor in latency for high batch sizes and long context lengths. Bifurcated attention achieves this by dividing the attention mechanism during incremental decoding into two distinct GEMM operations,
Resources
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
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Supporting research at academic institutions and non-profit organizations in areas that align with our mission to advance customer-obsessed science.