Large Language Model
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images Supplementary Materials
Fish-10K was manually curated with the help of biological experts co-authored in this paper.
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
Hence, we consider asking a VLM to provide the scientific name of the organism shown in a given image. There are two types of questions that we consider for this task. First, we consider open-ended questions, where we do not provide any answer choices (or options) to the VLM in the input prompt.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting Jialin Chen 1, Jan Eric Lenssen
Time series forecasting has attracted significant attention in recent decades.
Self-Guiding Exploration for Combinatorial Problems
They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Refinement to effectively navigate and solve intricate tasks.
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
We leverage ideas from Stochastic Realization Theory to develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an elementary composable module. The overall architecture can be used to implement models that can access short-term eidetic memory "in-context," permanent structural memory "in-weights,"
Probing the Decision Boundaries of In-context Learning in Large Language Models
Recent language models, such as GPT -3+ [Brown et al., 2020, Achiam et al., 2023], have demonstrated Recent attempts to understand in-context learning have focused on various aspects. On the practical side, research has investigated the impact of different factors on in-context learning.
Information Re-Organization Improves Reasoning in Large Language Models
Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest.
