Papers by Tingyu Xia
Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
| Approach: | They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method . |
| Outcome: | The proposed methods outperform random selection on large datasets on large data pools. |
FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks. |
| Approach: | They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus . |
| Outcome: | The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks. |
Large Language Model Evaluation via Matrix Nuclear-Norm (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are computationally intensive due to their O(n3) time complexity with Singular Value Decomposition (SVD). |
| Approach: | They propose a metric to quantify the data compression proficiency of large language models and a convex approximation of matrix rank to capture both predictive discriminability and diversity. |
| Outcome: | The proposed model achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as models increase from 111M to 6.7B . |
Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment. |
| Approach: | They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs. |
| Outcome: | The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger. |