Negative Transfer Paradigm

A Negative Transfer Paradigm is a learning paradigm that describes how knowledge acquired from a prior task or domain can impair the performance of a system on a new task, particularly in transfer learning or multi-task learning settings.

• AKA: Knowledge Interference, Harmful Transfer, Transfer Deterioration.
• Context:
  • It can occur when pretraining a language model on source domains introduces inductive biases misaligned with target text generation tasks.
  • It can reduce the effectiveness of fine-tuning in AI text generation when source and target domains differ stylistically or topically.
  • It can result from overly broad multi-task setups where unrelated or adversarial objectives interfere with target generation performance.
  • It can reflect mismatches in domain vocabulary, syntactic structures, or discourse patterns.
  • It can be diagnosed through performance drops in target-specific evaluation metrics (e.g., BLEU, ROUGE).
  • It can be mitigated using domain adaptation techniques, selective forgetting, or adversarial task balancing.
  • It can emerge in both supervised and unsupervised transfer settings (e.g., unsupervised pretraining).
  • ...
• Example(s):
  • Cross-Domain Language Model Pretraining that degrades quality in legal domain text generation.
  • Multi-Objective Fine-tuning where one objective (e.g., toxicity avoidance) dominates fluency learning.
  • Continual Learning in NLP where early exposure to biased news data reduces generalization to encyclopedic generation tasks.
  • ...
• Counter-Example(s):
  • Positive Transfer, which improves performance on new tasks by leveraging learned knowledge.
  • In-Domain Pretraining, which avoids domain mismatch and typically enhances downstream generation.
  • Zero-Shot Learning, which assumes no prior task-specific learning and avoids harmful priors.
  • ...
• See: Transfer Learning, Multi-Task Learning, Domain Adaptation, Catastrophic Forgetting, Fine-Tuning, AI Text Generation Task.

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References

2022

• (Wu et al., 2022) ⇒ Zirui Wu, Sheng Zhang, Yue Zhang, Jingjing Gong, & Xiaodong Gu. (2022). "Negative Transfer in Transfer Learning: A Survey". In: arXiv Preprint.
  • QUOTE: Negative Transfer refers to the phenomenon where knowledge transferred from a source domain impairs performance on a target domain in transfer learning. The survey systematically reviews definitions, causes, and mitigation strategies, highlighting that negative transfer is more likely when the source and target domains are less related or exhibit large distributional shifts. Approaches to avoid negative transfer include source selection, domain similarity measures, and robust transfer algorithms."

    "The paper also discusses open challenges, such as how to quantify and predict negative transfer, and the need for benchmarks to evaluate transfer learning methods under negative transfer risk.

2020

• (Mathur et al., 2020) ⇒ Nitika Mathur, Timothy Baldwin, & Trevor Cohn. (2020). "Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics". In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
  • QUOTE: The study highlights cases of negative transfer in automatic machine translation evaluation, where reliance on certain metrics leads to poorer system rankings than human evaluation. This demonstrates that negative transfer can occur not only in model adaptation but also in the transfer of evaluation protocols, resulting in misleading conclusions about system quality.

2019

• (Wang et al., 2019) ⇒ Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, & Wen Su. (2019). "MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network". In: arXiv Preprint.
  • QUOTE: Our MCNE framework supports transfer learning tasks with excellent interpretability and robustness, but we observe that transferring knowledge from unrelated behaviors or network aspects can result in negative transfer, degrading performance on the target task. The study emphasizes the importance of modeling multi-aspect preferences to reduce negative transfer in social network representation learning.

2010

• (Pan & Yang, 2010) ⇒ Sinno Jialin Pan & Qiang Yang. (2010). "A Survey on Transfer Learning". In: Journal of Machine Learning Research.
  • QUOTE: Negative transfer occurs when the knowledge transferred from the source task adversely affects the target task performance, which is a major challenge in transfer learning. The survey provides a taxonomy of transfer learning, discusses conditions under which negative transfer arises, and reviews strategies for mitigating its impact, such as careful selection of source data and adaptive transfer algorithms.

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