Papers with pre-training approaches
Context-Aware Transformer Pre-Training for Answer Sentence Selection (2023.acl-short)
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| Challenge: | Existing approaches to perform Answer Sentence Selection (AS2) using only the candidate sentence are sub-optimal. |
| Approach: | They propose to use pre-trained transformers to perform contextual AS2 fine-tuning . they propose to apply pre-training objectives to local contextual AS2. |
| Outcome: | The proposed methods improve baseline AS2 accuracy by up to 8% on some datasets. |
Multi-task Learning for Multilingual Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing multilingual neural machine translation systems rely on bitext training data, which is limited and costly to collect. |
| Approach: | They propose a multi-task learning framework that trains the model with the translation task on bitext data and two denoising tasks on monolingual data. |
| Outcome: | The proposed framework outperforms pre-training models for both NMT and cross-lingual transfer learning NLU tasks. |
DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have been used in real-world industrial scenarios for various natural language processing tasks, but their high inference cost makes their deployment impractical, necessitating the use of smaller models. |
| Approach: | They propose a continual pre-training technique that generates diverse task instructions and responses via reading comprehension on conversation transcripts, enabling better instruction generalization. |
| Outcome: | The proposed technique improves small LLMs’ domain adaptability for business conversational tasks, compared with traditional methods that rely on next-token prediction. |
Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis (2026.eacl-long)
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| Challenge: | Membership inference attacks (MIAs) reveal whether specific data was used to train machine learning models, serving as important tools for privacy auditing and compliance assessment. |
| Approach: | They propose to examine LLMs’ internal representations rather than just their outputs to gain additional insights into potential membership inference signals. |
| Outcome: | The proposed framework yields strong membership detection across several model families achieving average AUC scores of 0.85 on popular MIA benchmarks. |
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)
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| Challenge: | sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining. |
| Approach: | They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks. |
| Outcome: | The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks. |
Effective Pretraining Objectives for Transformer-based Autoencoders (2022.findings-emnlp)
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| Challenge: | ELECTRA is more accurate than BERT, but it is not clear if this is due to its innovative architecture or to the long and extensive training, which highly increases the computation cost for obtaining the final language model. |
| Approach: | They propose to replace BERT’s Masked Language Modeling objective (MLM) with Token Detection (TD) by using a statistical approach to generate light tokens. |
| Outcome: | The proposed method can replace ELECTRA's computationally heavy generators without a significant drop in performance. |
VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models (2024.findings-emnlp)
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| Challenge: | VE-KD is a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Approach: | They propose a method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. |
| Outcome: | VE-KD outperforms DistilBERT and Adapt-and-Distill in biomedical domain tasks . compared with other methods, it outperformed Distilbert and adapted-and distill . |