Papers with arithmetic operations
Structure Learning for Neural Module Networks (D19-64)
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| Challenge: | Neural Module Networks are a class of neural networks that involve human-specified neural modules . current models only learn the parameters of the modules and/or the order of their execution . |
| Approach: | They propose to learn internal structure and sequence without extra supervisory signals . they use dynamically composable modules which are then assembled into a layout . |
| Outcome: | The proposed model performs comparable to models using hand-designed modules. |
NumeroLogic: Number Encoding for Enhanced LLMs’ Numerical Reasoning (2024.emnlp-main)
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| Challenge: | Language models struggle with numerical and arithmetical tasks, such as multiplying 3-digit numbers. |
| Approach: | They propose a method to include the count of digits before each number instead of “42”. |
| Outcome: | The proposed format improves the reasoning process before generating the actual number. |
Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition (2022.lrec-1)
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| Challenge: | Large Language Models such as GPT-3 have demonstrated on-the-fly reasoning capabilities in NLP tasks, but they struggle with arithmetic operations. |
| Approach: | They propose a Transformer Language Model that decomposes numbers in units, tens, and so on . they introduce a pipeline that allows them to perform arithmetic operations between decomposed numbers . |
| Outcome: | The proposed model improves accuracy in addition, subtractions and multiplication tasks by 63% . the model is fine-tuned to perform arithmetic operations between decomposed numbers . |
On the Additive Compositionality of Task Vectors in Vision–Language Models (2026.eacl-short)
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| Challenge: | In-context learning (ICL) in large language models (LLMs) has been shown to operate through task vectors, but its extension to vision-language models (VLMs) remains underexplored. |
| Approach: | They construct visual reasoning tasks with clearly defined subtasks and extract task vectors from few-shot demonstrations. |
| Outcome: | The proposed model can be extended to vision-language models (VLMs) by adding the vectors of its constituent subtasks. |
AdaPT: A Set of Guidelines for Hyperbolic Multimodal Multilingual NLP (2024.findings-naacl)
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| Challenge: | Euclidean space is used for training neural models and performing arithmetic operations, but many data types have complex geometries and cannot be captured in the Euclidesan space. |
| Approach: | They propose a set of guidelines for initialization, parametrization, and training of neural networks that can be generalized over existing neural network training methodologies. |
| Outcome: | The proposed framework outperforms Euclidean methods on three tasks over 12 languages and modalities on a variety of domains. |
Beta-LR: Interpretable Logical Reasoning based on Beta Distribution (2024.findings-naacl)
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| Challenge: | Existing methods to capture logical information from text are limited by the uncertainty of the text. |
| Approach: | They propose a probabilistic embedding method to capture logical information from text . they embed texts into beta distributions on each dimension to eliminate logical uncertainty . |
| Outcome: | The proposed method achieves competitive performances on two datasets. |
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator (2025.emnlp-main)
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| Challenge: | Existing robot simulators focus on physical process modeling and realistic rendering, resulting in high computational costs and limited adaptability. |
| Approach: | They propose a modular and novel LLM-powered framework to analyze and validate robot behaviors in text-based environments. |
| Outcome: | The proposed framework can generalize across scenarios and achieve long-horizon complex simulation. |
Numerical reasoning in machine reading comprehension tasks: are we there yet? (2021.emnlp-main)
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| 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. |
Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models (2024.emnlp-main)
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| Challenge: | a new benchmark for evaluating the mathematical reasoning on large language models is being developed . popularity of reasoning benchmarks is leading to performance saturation and training set contamination. |
| Approach: | They introduce a benchmark for evaluating the mathematical reasoning on large language models . they find that models struggle with Mathador-LM, scoring lower than average 3rd graders . |
| Outcome: | The proposed benchmark improves performance on large language models . it also reduces test-set leakage into training data, a new study shows . |