2.5k citations · 3.4k across the 30 of their papers we have counts for
46 papers · 1 filter
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
Woojeong Jin, Subhabrata Mukherjee, Yu Cheng +5
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks. However, such generalization to vision-l…
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
Zhihong Shao, Yeyun Gong, Yelong Shen +3
Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connectin…
Skill-Based Few-Shot Selection for In-Context Learning
Shengnan An, Bo Zhou, Zeqi Lin +5
In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each…
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong +4
Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts…
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Tong Wu, Zhihao Fan, Xiao Liu +9
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generatio…
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo +6
Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intell…