11 citations · 28 across the 8 of their papers we have counts for
8 papers
LZ Penalty: An information-theoretic repetition penalty for autoregressive language models
Antonio A. Ginart, Naveen Kodali, Jason Lee +3
We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capability. The penalty is based on the cod…
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs
Michael S. Ryoo, Honglu Zhou, Shrikant Kendre +9
We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos, particularly designed to efficiently capture temporal information over multiple frames. BLIP-3-Video…
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
Le Xue, Manli Shu, Anas Awadalla +30
This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
Jianguo Zhang, Tian Lan, Rithesh Murthy +15
Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presen…
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Jianguo Zhang, Kun Qian, Zhiwei Liu +7
Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversi…
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Tianlu Wang, Xi Victoria Lin, Nazneen Fatema Rajani +3
Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models. Some commonly adopted debiasing approaches, inc…
Proposal Learning for Semi-Supervised Object Detection
Peng Tang, Chetan Ramaiah, Yan Wang +2
In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled…
Active Clustering with Model-Based Uncertainty Reduction
Caiming Xiong, David Johnson, Jason J. Corso
Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningf…