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Found 6 papers, 1 papers with code
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings
However, due to the token bias in pretrained language models, the models can not capture the fine-grained semantics in sentences, which leads to poor predictions.
Balancing the Causal Effects in Class-Incremental Learning
In this way, the model is encouraged to adapt to all classes with causal effects from both new and old data and thus alleviates the causal imbalance problem.
Class Incremental Learning
Continual Named Entity Recognition
+6
Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer
The discrepancy results in the model learning irrelevant information for old and pre-trained tasks, which leads to catastrophic forgetting and negative forward transfer.
Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs
Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation.
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
Perturbation-based Self-supervised Attention for Attention Bias in Text Classification
In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn.
