Papers by Qinghai Miao
Rank-Awareness and Angular Constraints: A New Perspective on Learning Sentence Embeddings from NLI Data (2025.emnlp-main)
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| Challenge: | High-quality sentence embeddings are critical for advancing a wide range of Natural Language Processing tasks. |
| Approach: | They propose a framework that leverages the full NLI dataset augmented with pre-computed continuous similarity scores (S) they employ a Rank Margin objective that enforces rank consistency against S using an explicit margin and a Gated Angular objective that conditionally refines embedding geometry based on NLI label (L) and S score agreement. |
| Outcome: | The proposed framework outperforms baseline models on STS and the MTEB benchmarks. |
DimA: A Parameter-efficient Fine-tuning Method with Knowledge Transfer Based on Transformer (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) have demonstrated impressive performance across various downstream tasks, but fine-tuning is computationally expensive and storage-intensive. |
| Approach: | They propose a parameter-efficient method called DimA which enhances the transformer architecture by increasing the dimensionality. |
| Outcome: | The proposed method achieves state-of-the-art results in GLUE and XSUM tasks while utilizing less than 1% of the original model’s parameters. |
Tree-Notebook: A Context-Aware Agent with Tree Search and Entropy-Aware Data Shadow for Interactive Data Science (2026.findings-acl)
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| Challenge: | Experimental results show that Tree-Notebook achieves state-of-the-art (SOTA) performance on InfiAgent-DABench and DSBench. |
| Approach: | They propose an agentic framework that mimics the iterative cognitive process of human data scientists. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on InfiAgent-DABench and DSBench. |