Papers with language modeling capabilities
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks (2023.findings-acl)
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| Challenge: | Existing semi-parametric language models lack the capacity to perform zero-shot tasks . large language models have shown impressive zero-shoot ability, but huge model size costs . semi-parametric language model can be used to augment a smaller language model with retrieved background knowledge . |
| Approach: | They propose a semi-parametric language model for zero-shot task generalization that augments a smaller language model with retrieved related background knowledge. |
| Outcome: | The proposed model outperforms T0-3B by 16% across seven diverse evaluation tasks while being 3.8x smaller in scale. |
Deciphering Stereotypes in Pre-Trained Language Models (2023.emnlp-main)
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Weicheng Ma, Henry Scheible, Brian Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi
| Challenge: | Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples. |
| Approach: | They propose a framework for examining stereotype-encoding behavior of PLMs using model probing and textual analyses. |
| Outcome: | The proposed approach can debiase PLMs without compromising their language modeling capabilities or performance. |
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods struggle to effectively propagate updates to interconnected facts, limiting the performance of reasoning tasks based on these updated facts. |
| Approach: | They propose a reasoning-based benchmark, ReCoE, which covers six common reasoning schemes in the real world. |
| Outcome: | The proposed reasoning-based benchmark shows that current models struggle to propagate updated knowledge within reasoning schemes. |
Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher (2024.findings-acl)
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| Challenge: | Chinese Spelling Correction models are prone to over-correct and poor generalization for error patterns outside the standard distribution. |
| Approach: | They propose a teacher network guided by prior knowledge for distillation learning of CSC models. |
| Outcome: | The proposed method significantly enhances the CSC model’s language modeling capabilities, crucial for minimizing over-correction. |
UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding (2026.findings-acl)
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| Challenge: | Consistency models (CMs) have shown promise in the efficient generation of both image and text. |
| Approach: | They propose to use a discrete token for both image and text generation to achieve a unified denoising perspective. |
| Outcome: | The proposed model outperforms SD3 on GenEval and Image Reward while being 1.5 faster at long-sequence generating speed. |