Amazon outperforms GPT-3.5 by 16%

Amazon's model, trained with 1 billion parameters, has surpassed the previous state-of-the-art LLM (#GPT-3.5) by 16%, achieving a remarkable accuracy rate of 91.68% compared to the GPT's rate of 75.17%.  

Study: https://lnkd.in/g2Xn446256434259.5K

This impressive feat was achieved by generating intermediate reasoning steps for prompting demonstrations, also known as chain-of-thought (#CoT) prompting.

The model is enhanced with an additional layer that enables it to produce more insightful rationales and answers based on a broader range of knowledge.  The study, code, and datasets are publicly available:  

Code: https://lnkd.in/gJ7BxVJJ

Datasets: https://lnkd.in/gdp6NF9k#llm

Instructions To Get Started

GitHub - amazon-science/mm-cot: Official implementation for “Multimodal Chain-of-Thought Reasoning in Language Models” (stay tuned and more will be updated)

Official implementation for "Multimodal Chain-of-Thought Reasoning in Language Models" (stay tuned and more will be updated) - GitHub - amazon-science/mm-cot: Official implementation for ...

GitHubamazon-science

Training

# rationale generation
CUDA_VISIBLE_DEVICES=0,1 python main.py \
    --model allenai/unifiedqa-t5-base \
    --user_msg rationale --img_type detr \
    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
    --final_eval --prompt_format QCM-LE

# answer inference
CUDA_VISIBLE_DEVICES=0,1 python main.py \
    --model allenai/unifiedqa-t5-base \
    --user_msg answer --img_type detr \
    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
    --final_eval --prompt_format QCMG-A \
    --eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
    --test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json

Inference

Our trained models are available at models. To use trained models, please put the them under the models folder.

# rationale generation
CUDA_VISIBLE_DEVICES=0,1 python main.py \
    --model allenai/unifiedqa-t5-base \
    --user_msg rationale --img_type detr \
    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
    --final_eval --prompt_format QCM-LE \
    --evaluate_dir models/MM-CoT-UnifiedQA-base-Rationale

# answer inference
CUDA_VISIBLE_DEVICES=0,1 python main.py \
    --model allenai/unifiedqa-t5-base \
    --user_msg answer --img_type detr \
    --bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
    --final_eval --prompt_format QCMG-A \
    --eval_le models/rationale/predictions_ans_eval.json \
    --test_le models/rationale/predictions_ans_test.json \
    --evaluate_dir models/MM-CoT-UnifiedQA-base-Answer