Tag: faang

Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical …

Read more “Evaluating Evaluation Metrics — The Mirage of Hallucination Detection”

Building and scaling generative AI models demands enormous resources, but this process can get tedious. Developers wrestle with managing job queues, provisioning clusters, and resolving dependencies just to ensure consistent results. This infrastructure overhead, along with the difficulty of discovering the optimal training recipe and navigating the endless maze of hyperparameter and model architecture choices, …

Read more “Announcing new capabilities in Vertex AI Training for large-scale training”

A dangerous assumption that can be made from prior work on the bias transfer hypothesis (BTH) is that biases do not transfer from pre-trained large language models (LLMs) to adapted models. We invalidate this assumption by studying the BTH in causal models under prompt adaptations, as prompting is an extremely popular and accessible adaptation strategy …

Read more “Bias after Prompting: Persistent Discrimination in Large Language Models”

Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya (*The work was done when Keertana interned at Netflix.) Introduction This blog focuses on post-training generative recommender systems. Generative recommenders (GRs) represent a new paradigm in the field of recommendation systems (e.g. HSTU, OneRec). These models draw inspiration from recent advancements in transformer architectures used for …

Read more “Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning”

We revisit scene-level 3D object detection as the output of an object-centric framework capable of both localization and mapping using 3D oriented boxes as the underlying geometric primitive. While existing 3D object detection approaches operate globally and implicitly rely on the a priori existence of metric camera poses, our method, Rooms from Motion (RfM) operates …

Read more “Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping”

The conversation around generative AI in the enterprise is getting creative.  Since launching our popular Nano Banana model, consumers have created 13 billion images and 230 million videos1. Enterprises can combine Gemini 2.5 Pro with our generative media models – Lyria, Chirp, Imagen, and Veo – to bring their ideas to life.  To us, generative …

Read more “5 ad agencies used Gemini 2.5 Pro and gen media models to create an “impossible ad””