Papers with ranking loss
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
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| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
| Approach: | They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT. |
| Outcome: | The proposed model has been successfully integrated into a commercial web search engine as of February 2024. |
Contrastive Fine-tuning Improves Robustness for Neural Rankers (2021.findings-acl)
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| Challenge: | Current state-of-the-art neural rankers can deteriorate when exposed to noisy inputs or applied to a new domain. |
| Approach: | They propose a contrastive loss and ranking loss method for fine-tuning rankers that combine ranking loss and rank loss to improve their robustness to query reformulations and noise perturbations. |
| Outcome: | The proposed method outperforms data augmentation for robustifying rankers on four passage ranking datasets. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Automated Student Assessment Prize dataset. |
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video (P19-1)
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| Challenge: | Existing techniques for weakly-supervised spatio-temporally grounding natural sentence in video are lacking . |
| Approach: | They propose a weakly-supervised task for spatially grounding sentences in video . they extract instances from video and encode them using attentive interactor . results demonstrate superiority of their proposed task over baseline approaches . |
| Outcome: | The proposed model outperforms baseline approaches in a weakly-supervised task . it can characterize reliable instance-sentence pairs and penalize unreliable ones . |
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (2022.emnlp-main)
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| Challenge: | Existing document-level relation extraction methods focus on fully supervised scenarios but in real-world, incomplete labeling is a common problem because the number of entity pairs grows quadratically with the number. |
| Approach: | They propose a positive-unlabeled learning framework for document-level relation extraction (RE) that uses shift and squared ranking loss positive- unlabeles (SSR-PU) learning to solve incomplete labeling problem. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under fully supervised and extremely unlabeled conditions and achieves 14 F1 points over the baseline with incomplete labeling. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning (2020.emnlp-main)
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| Challenge: | Existing methods for document summarization consider the informativeness of the assessed summary and require human-generated references for each test summary. |
| Approach: | They propose to evaluate summary qualities without reference summaries by unsupervised contrastive learning. |
| Outcome: | The proposed method outperforms other evaluation metrics even without reference summaries. |
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)
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| Challenge: | Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration. |
| Approach: | They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification. |
| Outcome: | The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions. |
Embracing Large Language Models in Traffic Flow Forecasting (2025.findings-acl)
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| Challenge: | Existing methods to predict future traffic flows capture spatio-temporal dependencies, but they fail to adapt to test-time environmental changes. |
| Approach: | They propose to use large language models to help traffic flow forecasting by capturing spatio-temporal dependencies and using a large language model to select the most likely result. |
| Outcome: | The proposed method is based on large language models (LLMs) and an LLM-based selector. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning (2025.acl-long)
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Chengwei Qin, Wenhan Xia, Fangkai Jiao, Chen Chen, Yuchen Hu, Bosheng Ding, Ruirui Chen, Shafiq Joty
| Challenge: | Existing methods to train student models on the generated outputs of teacher models are not efficient for ICL. |
| Approach: | They propose to align the output of smaller (student) models with that of larger (teacher) models by incorporating a ranking loss and aligning the token-level output distribution. |
| Outcome: | The proposed model outperforms baseline models on a variety of tasks involving language understanding, reasoning, and coding. |