Papers with language models fine-tuned
Phrase-level Textual Adversarial Attack with Label Preservation (2022.findings-naacl)
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| Challenge: | Existing adversarial attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality. |
| Approach: | They propose a phrase-level perturbation-based adversarial ATtack that generates adversarials through phrase- level perturbations. |
| Outcome: | The proposed approach improves the performance of natural language processing models by reducing the need for word-level perturbations and preserving the fluency and grammaticality of the samples. |
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry (2025.emnlp-industry)
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| Challenge: | Recent trends in NLP utilize knowledge graphs to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. |
| Approach: | They propose to use graph-aware neighborhood contrastive learning methodology SciNCL to enhance pretrained language models by incorporating additional knowledge from graph structures. |
| Outcome: | The proposed graph-aware neighborhood contrastive learning methodology outperforms a state-of-the-art mE5-large text encoder on the process industry text embedding benchmark while having 3 times fewer parameters. |
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation (2020.emnlp-main)
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| Challenge: | Existing datasets lack rich enough contexts to guide models and evaluations are unreliable for long-form creative text. |
| Approach: | They propose a dataset and evaluation platform built from STORIUM . their dataset contains 6K lengthy stories with fine-grained natural language annotations . |
| Outcome: | The proposed model can be used to generate 6K long stories with fine-grained natural language annotations and a user-generated dataset. |
Language Models are Few-Shot Butlers (2021.emnlp-main)
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| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
| Approach: | They propose a two-stage procedure to learn from a small set of demonstrations and a simple reinforcement learning algorithm to improve by interacting with an environment. |
| Outcome: | The proposed method improves with only 1.2% of the demonstrations and a simple reinforcement learning algorithm over existing methods in the ALFWorld environment. |