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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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Featured news
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IEEE International Conference on Machine Learning and Applications (ICMLA)2022In this article, we propose a new probabilistic approach for product recommendations using deep learning framework, combining information from historical observations, similar users and prior knowledge. The deep learning approach is using autoregressive recurrent networks to model the recommendations probabilistically from a Bernoulli distribution. If prior information exists we implement a Pseudo-Bayesian
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EMNLP 20222022Conversational understanding is an integral part of modern intelligent devices. In a large fraction of the global traffic from people using smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a user’s query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing. Such errors are compounded
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EMNLP 20222022With ever-growing digital adoption in the society and increasing demand for businesses to deliver to customers doorstep, the last mile hop of transportation planning poses unique challenges in emerging geographies with unstructured addresses. One of the crucial inputs to facilitate effective planning is the task of geolocating customer addresses. Existing systems operate by aggregating historical delivery
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EMNLP 20222022In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a novel Constrained Generation Framework (CGF) for query rewriting at both global and personalized levels. It is
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EMNLP 20222022Unexpected responses or repeated clarification questions from conversational agents detract from the users’ experience with technology meant to streamline their daily tasks. To reduce these frictions, Query Rewriting (QR) techniques replace transcripts of faulty queries with alternatives that lead to responses that satisfy the users’ needs. Despite their successes, existing QR approaches are limited in
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