Arithmetic Reasoning
70 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Arithmetic Reasoning
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Libraries
Use these libraries to find Arithmetic Reasoning models and implementations
GSM8K
MGSM
SMART-101
Most implemented papers
LLaMA: Open and Efficient Foundation Language Models
facebookresearch/llama
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arXiv 2023
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
Llemma: An Open Language Model For Mathematics
We present Llemma, a large language model for mathematics.
Large Language Models are Zero-Shot Reasoners
kojima-takeshi188/zero_shot_cot
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Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars.
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
wengsyx/neural-comprehension
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Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
PAL: Program-aided Language Models
srush/minichain
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Much of this success can be attributed to prompting methods such as "chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem.
Reasoning with Language Model Prompting: A Survey
zjunlp/Prompt4ReasoningPapers
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Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
Batch Prompting: Efficient Inference with Large Language Model APIs
We extensively validate the effectiveness of batch prompting on ten datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) reduces the LLM (Codex) inference token and time costs while achieving better or comparable performance.
