alexshengzhili (shengzhi alex li)

New activity in vidore/colqwen2-v1.0 4 days ago

Merged variant!

3

#1 opened 13 days ago by

michaelfeil

authored a paper 8 days ago

Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities

Paper • 2411.05232 • Published 17 days ago

liked a dataset 9 days ago

alexshengzhili/Abstract2Appendix_v1_10k

Viewer • Updated 9 days ago • 9.82k • 28 • 3

Reacted to their post with 🔥 9 days ago

Post

1103

We’re excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models!

🌟 Key Highlights:

• Expert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.
• Rich Metadata: we have aggregated the reviews, and also included full parsed paper
• LLM Ready: Perfect for fine-tuning (We did dpo and sft)

🎯 Use Cases:

• Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).
• Benchmarking zero-shot and long-context comprehension capabilities.

🔗 Explore the dataset: alexshengzhili/Abstract2Appendix_v1_10k

This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! https://arxiv.org/abs/2411.05232

posted an update 9 days ago

Post

1103

We’re excited to release Abstract2Appendix v1 10K , a high-quality dataset crafted to enhance the long-context capabilities of Large Language Models (LLMs). This dataset combines thousands of peer reviews from NeurIPS 2023, EMNLP 2023, TMLR, and ICLR 2023, making it a treasure trove of detailed feedback, critical reasoning, and structured academic insights. Our experiments showed that this dataset increased long context ability of phi-3 models!

🌟 Key Highlights:

• Expert Reviews: Aggregated from 3–6 reviews per paper, capturing the most insightful and constructive content.
• Rich Metadata: we have aggregated the reviews, and also included full parsed paper
• LLM Ready: Perfect for fine-tuning (We did dpo and sft)

🎯 Use Cases:

• Fine-tuning models with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT).
• Benchmarking zero-shot and long-context comprehension capabilities.

🔗 Explore the dataset: alexshengzhili/Abstract2Appendix_v1_10k

This dataset is based on the methodology described in our recent paper, “Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities”. Check it out for more details! https://arxiv.org/abs/2411.05232