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Math Word Problem Solving

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하위 태스크: 0
평가표: 13
보존된 평가 기록: 245

A math word problem is a mathematical exercise (such as in a textbook, worksheet, or exam) where significant background information on the problem is presented in ordinary language rather than in mathematical notation. As most word problems involve a narrative of some sort, they are sometimes referred to as story problems and may vary in the amount of technical language used.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/fajri91/indommlu

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flagalpha/llama2-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fsoft-ai4code/codecapybara

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gair-nlp/abel

Solving Quantitative Reasoning Problems with Language Models

https://github.com/gammatauai/leetcode-hard-gym

Sparks of Artificial General Intelligence: Early experiments with GPT-4

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gentopia-ai/gentopia

Let's Verify Step by Step

https://github.com/ggerganov/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ggml-org/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/glb400/Toy-RecLM

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/grantslatton/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

https://github.com/hkust-nlp/b-star

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

https://github.com/huberemanuel/DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/huggingface/transformers

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/idiap/abroad-re

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/jingyaogong/minimind

Mixtral of Experts

https://github.com/jlrussin/interpret-math-transformer

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/kamanphoebe/look-into-moes

Mixtral of Experts

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/knowlab/bi-weekly-paper-presentation

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/lijierui/group-attention

https://github.com/llamafamily/llama-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/longhao-chen/aicas2024

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mandubian/pytorch_math_dataset

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/meetyou-ai-lab/can-mc-evaluate-llms

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/mesotron/teaching_transformers

Analysing Mathematical Reasoning Abilities of Neural Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mgmalek/efficient_cross_entropy

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