5 papers · 1 filter
Procedural Knowledge at Scale Improves Reasoning
Di Wu, Devendra Singh Sachan, Wen-tau Yih +1
Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat each problem in isolation and do n…
ImpRAG: Retrieval-Augmented Generation with Implicit Queries
Wenzheng Zhang, Xi Victoria Lin, Karl Stratos +2
Retrieval-Augmented Generation (RAG) systems traditionally treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. This separation…
FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality
Mingda Chen, Yang Li, Xilun Chen +3
Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, le…
Improving Factuality with Explicit Working Memory
Mingda Chen, Yang Li, Karthik Padthe +5
Large language models can generate factually inaccurate content, a problem known as hallucination. Recent works have built upon retrieved-augmented generation to improve factuality…
Chameleon: Mixed-Modal Early-Fusion Foundation Models
Chameleon Team
We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable…