Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years.

The hotel and should The hotel should be in the and should have If there is no such hotel, how about one that has Once you find the you want to book it for <4 people> and <2 nights> starting from Y ou are also looking for a . The restaurant should be in the price range and should serve food The restaurant should be Once you find the you want to book a table for at <12:15>(use veiled expression)> on (Background) Once you have made a booking, you do not want to give out your email address for receiving orders.

The solution in [5,4]requires aone-pass forward computation of marginal probabilities.

Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years. However, the extent to which fundamental search and reasoning capabilities emerged within LLMs remains a topic of ongoing debate. In this work, we study if causal language modeling can learn a complex task such as solving Sudoku puzzles. To solve a Sudoku, the model is first required to search over all empty cells of the puzzle to decide on a cell to fill and then apply an appropriate strategy to fill the decided cell. Sometimes, the application of a strategy only results in thinning down the possible values in a cell rather than concluding the exact value of the cell.

Table I summarizes the datasets used for training and evaluation. Both baseline models and the PV AL framework were fine-tuned on 2,000 annotated tiles from Santa Ana, CA. The large-scale evaluation set includes about 100,000 tiles from Tempe and Santa Ana, while 480 tiles per region were used for cross-domain generalization tests across diverse climates and geographies. B. Multimodal LLM Configuration Configuring the PV AL system for solar panel detection involves a multi-faceted approach that integrates prompt engineering, output standardization, and supervised fine-tuning. This configuration is critical for steering the foundational GPT -4o model towards the specific, high-precision task of geospatial analysis. Prompt Task Decomposition Identify the presence of solar panels in images of residential rooftops, and determine their locations and quantity within the images. You will be provided with images that may contain residential rooftop solar systems. Analyze each image to detect solar panels. Steps: 1. ** Image Analysis **: Examine the entire image to identify any objects that appear to be solar panels.

Categorical attributes with qualitative values are ubiquitous in cluster analysis of real datasets. Unlike the Euclidean distance of numerical attributes, the categorical attributes lack well-defined relationships of their possible values (also called categories interchangeably), which hampers the exploration of compact categorical data clusters. Although most attempts are made for developing appropriate distance metrics, they typically assume a fixed topological relationship between categories when learning distance metrics, which limits their adaptability to varying cluster structures and often leads to suboptimal clustering performance. This paper, therefore, breaks the intrinsic relationship tie of attribute categories and learns customized distance metrics suitable for flexibly and accurately revealing various cluster distributions. As a result, the fitting ability of the clustering algorithm is significantly enhanced, benefiting from the learnable category relationships. Moreover, the learned category relationships are proved to be Euclidean distance metric-compatible, enabling a seamless extension to mixed datasets that include both numerical and categorical attributes. Comparative experiments on 12 real benchmark datasets with significance tests show the superior clustering accuracy of the proposed method with an average ranking of 1.25, which is significantly higher than the 5.21 ranking of the current best-performing method. Code and extended version with detailed proofs are provided below.

Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years.

The hotel and should The hotel should be in the and should have If there is no such hotel, how about one that has Once you find the you want to book it for <4 people> and <2 nights> starting from Y ou are also looking for a . The restaurant should be in the price range and should serve food The restaurant should be Once you find the you want to book a table for at <12:15>(use veiled expression)> on (Background) Once you have made a booking, you do not want to give out your email address for receiving orders.