Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks mainly focus more on the end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. Instead, we introduce WE-MATH, the first benchmark specifically designed to explore the problem-solving principles. We meticulously collect 6.5K visual math problems and decompose them into 10.9K step-level questions for evaluation, spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. Specifically, we decompose composite problems into sub-problems according to the required knowledge concepts and introduce a novel four-dimensional metric to hierarchically assess inherent issues in LMMs’ reasoning process. With WE-MATH, we conduct a thorough evaluation of existing LMMs in visual mathematical reasoning and provide comprehensive analysis and insight for future development. We anticipate that WE-MATH will open new pathways for advancements in visual mathematical reasoning for LMMs. Data and code are available at https://github.com/We-Math/We-Math.

Oracle Bone Script (OBS) is a vital treasure of human civilization, rich in insights from ancient societies. However, the evolution of written language over millennia complicates its decipherment. In this paper, we propose V-Oracle, an innovative framework that utilizes Large Multi-modal Models (LMMs) for interpreting OBS. V-Oracle applies principles of pictographic character formation and frames the task as a visual question-answering (VQA) problem, establishing a multi-step reasoning chain. It proposes a multi-dimensional data augmentation for synthesizing high-quality OBS samples, and also implements a multi-phase oracle alignment tuning to improve LMMs’ visual reasoning capabilities. Moreover, to bridge the evaluation gap in the OBS field, we further introduce Oracle-Bench, a comprehensive benchmark that emphasizes process-oriented assessment and incorporates both standard and out-of-distribution setups for realistic evaluation. Extensive experimental results can demonstrate the effectiveness of our method in providing quantitative analyses and superior deciphering capability.

In this paper, we propose a novel span-level model for Aspect-Based Sentiment Analysis (ABSA), which aims at identifying the sentiment polarity of the given aspect. In contrast to conventional ABSA models that focus on modeling the word-level dependencies between an aspect and its corresponding opinion expressions, in this paper, we propose Table Filling BERT (TF-BERT), which considers the consistency of multi-word opinion expressions at the span-level. Specially, we learn the span representations with a table filling method, by constructing an upper triangular table for each sentiment polarity, of which the elements represent the sentiment intensity of the specific sentiment polarity for all spans in the sentence. Two methods are then proposed, including table-decoding and table-aggregation, to filter out target spans or aggregate each table for sentiment polarity classification. In addition, we design a sentiment consistency regularizer to guarantee the sentiment consistency of each span for different sentiment polarities. Experimental results on three benchmarks demonstrate the effectiveness of our proposed model.