Papers with adversarial training framework
Improving Word Sense Induction through Adversarial Forgetting of Morphosyntactic Information (2024.starsem-1)
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| Challenge: | Contextualized word representations from pre-trained language models encode more information than is necessary for the identification of word senses and some of this information affect performance negatively in unsupervised settings. |
| Approach: | They propose to use a framework to erase specific information from pre-trained word models and create feature-invariant representations that are invariant to these ‘nuisance features’. |
| Outcome: | The proposed framework erases information from the representations of pre-trained language models, thereby creating feature-invariant representations. |
Domain-agnostic Question-Answering with Adversarial Training (D19-58)
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| Challenge: | Adapting models to new domain without finetuning is a challenging problem in deep learning. |
| Approach: | They propose an adversarial training framework for domain generalization in Question Answering task using a conventional QA model and a discriminator. |
| Outcome: | The proposed model outperforms the baseline model on Question Answering (QA) task. |
Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction (2023.findings-eacl)
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| Challenge: | Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. |
| Approach: | They propose a model architecture that jointly encodes a source language input sentence with its translation to the target language during training and takes a target language sentence with it as input during evaluation. |
| Outcome: | The proposed model architecture can integrate machine translation to improve event extraction while adding machine-translated data yields unstable performance due to representational gap. |
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)
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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang
| Challenge: | Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks. |
| Approach: | They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes. |
| Outcome: | The proposed training framework significantly improves upon translation baselines. |
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)
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| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
| Approach: | They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment. |
| Outcome: | The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness. |