Papers with learning strategies
Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
| Challenge: | Existing surveys focus on LLMs' specific utility for data annotation and synthesis. |
| Approach: | They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations . |
| Outcome: | The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information. |
What to Learn, and How: Toward Effective Learning from Rationales (2022.findings-acl)
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| Challenge: | Increasing interest in learning from rationales has led to the use of human-annotated explanations to inject useful inductive biases into models. |
| Approach: | They propose several novel loss functions and learning strategies to exploit human rationales to augment model prediction accuracy. |
| Outcome: | The proposed learning strategies improve on three datasets with human rationales and show that they are more efficient than baselines. |
BIPED: Pedagogically Informed Tutoring System for ESL Education (2024.acl-long)
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| Challenge: | Existing Large Language Models (LLMs) are limited in scope and lack pedagogical depth. |
| Approach: | They construct a BIlingual PEDagogically-informed Tutoring Dataset of one-on-one, human-to-human tutoring interactions using a post-hoc analysis. |
| Outcome: | The proposed models replicate the style of human teachers and employ diverse and contextually appropriate pedagogical strategies. |
Multi-target Backdoor Attacks for Code Pre-trained Models (2023.acl-long)
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| Challenge: | Existing work for backdoor attacks on neural code models insert triggers into task-specific data for code-related downstream tasks, limiting the scope of attacks. |
| Approach: | They propose task-agnostic backdoor attacks for code pre-trained models . they use two learning strategies to implant backdoors into code understanding and generation models - Poisoned Seq2Seq learning and token representation learning . |
| Outcome: | The proposed model is pre-trained with two learning strategies to support the multi-target attack of downstream code understanding and generation tasks. |
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements (2020.emnlp-main)
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| Challenge: | Existing tools for examining and fixing missing captions are lacking in mobile UIs. |
| Approach: | They propose a task for automatically generating language descriptions for UI elements from multimodal input including both the image and structural representations of user interfaces. |
| Outcome: | The proposed task can generate captions from image and structural representations of UI elements. |
Towards Objective Fine-tuning: How LLMs’ Prior Knowledge Causes Potential Poor Calibration? (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled powerful domain-specific applications through supervised fine-tuning. |
| Approach: | They propose a cognition-aware framework that applies targeted learning strategies according to the model’s prior knowledge to improve calibration. |
| Outcome: | The proposed framework significantly improves calibration while maintaining performance, achieving an average 57% reduction in ECE compared to standard fine-tuning in Llama3-8B. |
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
| Outcome: | The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets. |
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model (2024.findings-acl)
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| Challenge: | Large language models lack explicit alignment between source and target contexts, leading to unfaithful translations. |
| Approach: | They propose three learning strategies to encourage LLMs to pay more attention to source context . they use a dataset to test the effectiveness of their model across multiple language pairs . |
| Outcome: | The proposed model reduces hallucinatory translation and improves fidelity across multiple languages. |
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains (2023.acl-long)
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Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
| Challenge: | Recent studies have shown that pre-trained language models improve performance on a wide range of NLP tasks. |
| Approach: | They propose to use pre-trained language models to train medical domains on French language to compare performance with specialized ones. |
| Outcome: | The proposed models can take advantage of existing biomedical models in a foreign language by further pre-training them on our targeted data. |
A Comprehensive Survey on Learning from Rewards for Large Language Models: Reward Models and Learning Strategies (2025.findings-emnlp)
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| Challenge: | Recent developments in Large Language Models have shifted from pre-training to post-training and test-time scaling. |
| Approach: | They present a comprehensive overview of learning from rewards from the perspective of reward models and learning strategies across training, inference, and post-inference stages. |
| Outcome: | The proposed paradigm enables the transition from passive learning from static data to active learning from dynamic feedback. |
Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)
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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun
| Challenge: | Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem. |
| Approach: | They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules. |
| Outcome: | The results show that pre-trained language models are forgetful and pre-training leads to retentive models . |