Papers by Xiaoqi Qiu
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models (2024.findings-emnlp)
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| Challenge: | Developing long-context LLMs with robust long-text capabilities is underdeveloped due to a lack of benchmarks. |
| Approach: | They propose a Chinese benchmark for evaluating long-context LLMs with Chinese capabilities. |
| Outcome: | The proposed model is based on 6 open-source LLMs and 2 commercial ones. |
A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |