1 citations · 1 across the 4 of their papers we have counts for
4 papers
Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA
Sunah O, Jay-Yoon Lee
In multimodal multi-hop question answering, we focus on the initial retrieval stage via two distinct tasks: (1) evidence set completion, retrieving missing evidence given context,…
EMO: Pretraining Mixture of Experts for Emergent Modularity
Ryan Wang, Akshita Bhagia, Sewon Min
Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of capabilities, e.g., code, math, or…
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia +3
Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from scratch is expensive and scale…
Re-Examining Calibration: The Case of Question Answering
Chenglei Si, Chen Zhao, Sewon Min +1
For users to trust model predictions, they need to understand model outputs, particularly their confidence - calibration aims to adjust (calibrate) models' confidence to match expe…