6 papers
Optimal Bayesian Stopping for Efficient Inference of Consistent LLM Answers
Jingkai Huang, Will Ma, Zhengyuan Zhou
A simple strategy for improving LLM accuracy, especially in math and reasoning problems, is to sample multiple responses and submit the answer most consistently reached. In this pa…
BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning
Lin Sun, Linglin Zhang, Jingang Huang +3
We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget is allocated across documents a…
MagicWand: A Universal Agent for Generation and Evaluation Aligned with User Preference
Zitong Xu, Dake Shen, Yaosong Du +3
Recent advances in AIGC (Artificial Intelligence Generated Content) models have enabled significant progress in image and video generation. However, users still struggle to obtain…
NeMo: Needle in a Montage for Video-Language Understanding
Zi-Yuan Hu, Shuo Liang, Duo Zheng +10
Recent advances in video large language models (VideoLLMs) call for new evaluation protocols and benchmarks for video-language understanding. Inspired by the needle in a haystack t…
DuetRAG: Collaborative Retrieval-Augmented Generation
Dian Jiao, Li Cai, Jingsheng Huang +3
Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive task…
Contextual Data Augmentation for Task-Oriented Dialog Systems
Dustin Axman, Avik Ray, Shubham Garg +1
Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has bee…