Top Tweets for #ScienceQA
🧵 3/N We conducted extensive experiments on 7 vision-language benchmarks, including #ScienceQA, #TextVQA, #ChartQA, LLaVA-Bench, #MMBench, MM-Vet, and #MathVista.
STIC achieves consistent and significant performance improvements, with an average accuracy gain of 4.0% over the base LVLM and a notable gain of 6.4% on ScienceQA.
Here we explored language adaptations to different education levels for general science QA.
#education #LLMs #scienceQA
#MemoryMonday #NLProc
"Know Your Audience" by Donya Rooein et al. evaluates LLM adaptability to diverse age and education levels in science questions, highlighting the need for better adaptation in education settings.
https://t.co/9luwb7SRt6
🚀 @google is introducing new updates to aid in learning math and science, especially in visual contexts: https://t.co/qrBsiXy0v8.
💥 We're proud to spotlight our commitment to math and science over the past years, with projects like #MathVista, #Chameleon, and #ScienceQA.
1️⃣ MathVista: A 112-page study of evaluating math reasoning in visual contexts, with 12 large models such as #GPT_4V and #Bard on our new benchmark. https://t.co/kf2dU6ATDn
2️⃣ Chameleon: A framework that integrates various tools for math and science problems. https://t.co/pzfCQvddAR
3️⃣ ScienceQA: A multimodal benchmark for science, featuring annotations of lectures and solutions. https://t.co/dfTC0EFU8l
4️⃣ SciBench: A college-level benchmark focusing on science. https://t.co/0CHtkxbZZa
5️⃣ TheoremQA: a college-level benchmark for math reasoning, emphasizing theorem applications. https://t.co/E6zTZck5ns
6️⃣ Geometry3K: A benchmark for geometry problems, complemented with parsing annotations of logical forms and our leading neuro-symbolic approach. https://t.co/Na9OpsqZpO
Dive deeper with:
7️⃣ PromptPG/TabMWP: https://t.co/bLetcMfWed
8️⃣ DL4Math: https://t.co/ywDiWaA6Yu
9️⃣ Lila: https://t.co/X2v8Rpjk0d
🔟 IconQA: https://t.co/PkDNYVFxkl
*️⃣ UniGeo: https://t.co/3kNXAEm5KP
🚀 Google is introducing new updates to aid in learning math and science, especially in visual contexts.
💥 We're proud to spotlight our commitment to math and science over the past years, with projects like #MathVista, #Chameleon, and #ScienceQA.
1️⃣ MathVista: A 112-page study of evaluating math reasoning in visual contexts, with 12 large models such as #GPT_4V and #Bard on our new benchmark. https://t.co/kf2dU6ATDn
2️⃣ Chameleon: A framework that integrates various tools for math and science problems. https://t.co/pzfCQvddAR
3️⃣ ScienceQA: A multimodal benchmark for science, featuring annotations of lectures and solutions. https://t.co/dfTC0EFU8l
4️⃣ SciBench: A college-level benchmark focusing on science. https://t.co/0CHtkxbZZa
5️⃣ TheoremQA: a college-level benchmark for math reasoning, emphasizing theorem applications. https://t.co/E6zTZck5ns
6️⃣ Geometry3K: A benchmark for geometry problems, complemented with parsing annotations of logical forms and our leading neuro-symbolic approach. https://t.co/Na9OpsqZpO
Dive deeper with:
7️⃣ PromptPG/TabMWP: https://t.co/bLetcMfWed
8️⃣ DL4Math: https://t.co/ywDiWaA6Yu
9️⃣ Lila: https://t.co/X2v8Rpjk0d
🔟 IconQA: https://t.co/PkDNYVFxkl
*️⃣ UniGeo: https://t.co/3kNXAEm5KP
@google https://t.co/qrBsiXy0v8
We showcase Chameleon's applications on #ScienceQA, a multi-modal QA benchmark that spans numerous scientific topics and diverse contexts. By incorporating GPT-4 as the planner, our model achieves an impressive 86.54% score, setting a new SOTA in the few-shot scenario!🚀🧵1/6
🚀Meet Chameleon! An innovative plug-and-play framework enhancing #GPT4 and #ChatGPT like #AutoGPT for compositional reasoning, blending off-the-shelf tools with tailored LLM models 🔧✨🧠. New SOTA on #ScienceQA and TabMWP! 📈
🔗https://t.co/IBCvtHfz7Q
📜https://t.co/b0fETXZHfq
With a mere 1.2 million learnable parameters, LLaMA-Adapter demonstrates superior reasoning capacity on #ScienceQA, surpassing a diverse range of multi-modal and LLM models, such as fully-finetuned MM-COT and few-shot GPT-3.
LLaMA-Adapter can be simply extended to multi-modal input, e.g., images, for image-conditioned LLaMA, which achieves superior reasoning capacity on #ScienceQA (https://t.co/iSGK3SgpDM), a recent multi-modal science question benchmark.
📢Great news! Our #ScienceQA dataset is gaining significant attention lately. It is the primary benchmark for the next-gen #MultimodalCoT reasoning system by @AmazonScience, and it's now included in @huggingface: https://t.co/T2iPq6YIxP.
More details: 👉https://t.co/rAliqwfy4Z
#ChatGPT competitors: #Amazon jumps into fray with generative AI better than GPT-3.5
Will this be a #scienceQA benchmark?
Excited to be at #AAAI23 on-site! Can't wait to catch up with old friends and make new ones.
📢I'll give an oral presentation on #ScienceQA (https://t.co/tl4ONwBm9R) at @knowledgenlp Workshop on Monday, Feb 13, 2:15-3:15 pm in Room 144B.
If you're around, let's grab a coffee!
Congrats! Thank you @zhangzhuosheng for your kind words! I am glad that our work on #ScienceQA has helped you.
🏖️Multimodal Chain-of-Thought Reasoning in Language Models
https://t.co/L0husTxDDJ
🛠️Code & model:
https://t.co/GBEa81wJU6
💡Thank @lupantech for providing model info on ScienceQA!
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