Large Language Model
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
As the saying goes, "an image is worth a thousand words".
Finding Transformer Circuits with Edge Pruning
Recent work has automated the task of discovering circuits.
Ad Auctions for LLMs via Retrieval Augmented Generation
In the field of computational advertising, the integration of ads into the outputs of large language models (LLMs) presents an opportunity to support these services without compromising content integrity.
The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models
Despite their different natures, these strategies often lead to comparable performance gains.
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Work done partially during Yilun's internship at Amazon.
Mars: Situated Inductive Reasoning in an Open-World Environment Xiaojuan Tang
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Y et, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning with the acquired knowledge-- situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.
Combining Observational Data and Language for Species Range Estimation Max Hamilton 1 Christian Lange 2 Elijah Cole 3 Alexander Shepard 4
Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.