Papers with sub-modules
Variational Knowledge Graph Reasoning (N18-1)
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| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients (2024.emnlp-main)
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| Challenge: | Recent studies show that distributed machine learning is vulnerable to gradient inversion attacks . a recent study demonstrated the possibility of reconstructing private textual training data using partial gradients . |
| Approach: | They propose to use partial gradients to reconstruct training data using a Transformer model. |
| Outcome: | The proposed method is vulnerable to gradient inversion attacks, the authors show . they show that applying differential privacy on gradients during training offers limited protection . |
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2023.acl-long)
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| Challenge: | Recent advances in automated reasoning with natural text suffer from a combinatorial explosion of the search space and high failure rates for problems requiring longer chains of reasoning. |
| Approach: | They propose a Backward Chaining algorithm that decomposes reasoning into four sub-modules and implements it by few-shot prompted LLM inference. |
| Outcome: | The proposed algorithm achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets. |
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation (2020.coling-main)
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| Challenge: | Mongolian morphological segmentation is a crucial preprocessing step in many Mongolian related NLP applications. |
| Approach: | They propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation. |
| Outcome: | The proposed network is compared with baselines and evaluates its performance. |
TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA by Content Planning and Execution-based Reasoning (2024.acl-long)
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| Challenge: | Long-form table question answering often generates paragraph long and complex answers . a prevalent and concerning issue is hallucination, where models generate answers that are coherent yet factually incorrect or irrelevant to the input context. |
| Approach: | They propose a modular framework that decomposes the whole process into three sub-modules . framework produces a QA-based plan first, followed by generating an answer conditioned on this plan . human evaluation results indicate the framework improves strong baselines on accuracy and truthfulness . |
| Outcome: | The proposed framework improves accuracy and truthfulness on the FeTaQA and QTSumm datasets. |
Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning (2025.emnlp-main)
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| Challenge: | A sparse Mixture-of-Experts architecture has emerged as a highly scalable solution for instruction tuning. |
| Approach: | They propose a mixture-of-Clustered-Experts (MoCE) architecture that allows expert specialization . they evaluate the mechanism on a set of benchmarks and show its superiority . |
| Outcome: | The proposed approach outperforms existing models and benchmarks on instruction tuning scenarios with significant input heterogeneity. |