Papers with counting
Logical Inference for Counting on Semi-structured Tables (2022.acl-srw)
Copied to clipboard
| Challenge: | Natural Language Inference (NLI) tasks require numerical understanding to perform a numerical type of inference, such as counting. |
| Approach: | They propose a logical inference system for reasoning between semi-structured tables and texts that uses logical representations as meaning representations and model checking to handle a numerical type of inference. |
| Outcome: | The proposed system can perform inference with numerical comparatives with tables and texts in English. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
Copied to clipboard
Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
Copied to clipboard
| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Multimodal Language Models See Better When They Look Shallower (2025.emnlp-main)
Copied to clipboard
Haoran Chen, Junyan Lin, Xinghao Chen, Yue Fan, Jianfeng Dong, Xin Jin, Hui Su, Jinlan Fu, Xiaoyu Shen
| Challenge: | Existing studies show that multimodal large language models extract visual features from the final layers of a pretrained Vision Transformer. |
| Approach: | They propose a feature fusion method that strategically incorporates shallower layers . they propose MLLMs that extract visual features from the final layers of a pretrained Vision Transformer . |
| Outcome: | The proposed method outperforms deep layers on fine-grained visual tasks . it is the first comprehensive study of visual layer selection for MLLMs . |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
Copied to clipboard
Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
Numerical reasoning in machine reading comprehension tasks: are we there yet? (2021.emnlp-main)
Copied to clipboard
| Challenge: | Numerical reasoning based machine reading comprehension models have achieved near-human performance on a variety of benchmarks, but are they capable of learning to reason? |
| Approach: | They propose to use a DROP benchmark to measure machine reading comprehension and investigate models that have achieved near-human performance over standard metrics. |
| Outcome: | The DROP benchmark has inspired the design of specialized BERT and embedding the results into a specialized model. |
Extending First-Order Logic for Factual Reasoning over Knowledge Graphs (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for factual reasoning over knowledge graphs lack support for multiple quantifiers and connectives. |
| Approach: | They propose an extended FOL -structure over knowledge graphs that incorporates comparison predicates and counting quantifiers. |
| Outcome: | The proposed method achieves state-of-the-art on Fact-FOLX-KG, while previous methods experience performance drop on claims requiring comparison and counting. |
Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy (2026.findings-acl)
Copied to clipboard
Hosein Hasani, Mohammadali Banayeeanzade, Ali Nafisi, Sadegh Mohammadian, Fatemeh Askari, Mobin Bagherian, Amirmohammad Izadi, Mahdieh Soleymani Baghshah
| Challenge: | Large language models exhibit systematic limitations in counting tasks due to depth constraints. |
| Approach: | They propose a method that decomposes large counting tasks into smaller, independent sub-problems that the model can reliably solve. |
| Outcome: | The proposed method surpasses architectural limitations and achieves higher accuracy on large-scale counting tasks. |