Papers with multilayer perceptron
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation (2020.aacl-main)
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| Challenge: | Existing models that learn accurate representations of users and items are based on ratings, which oversimplify user preferences and item characteristics. |
| Approach: | They propose a novel, accurate, and explainable recommender model that integrates three key elements: BERT, multilayer perceptron, and maximum subarray problem to derive contextualized review features, model user-item interactions, and generate explanations. |
| Outcome: | The proposed model outperforms state-of-the-art models by an improvement gain of nearly 7% based on the human judges’ assessment . |
The Subject Annotations of the Danish Parliament Corpus (2009-2017) - Evaluated with Automatic Multi-label Classification (2022.lrec-1)
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| Challenge: | The interest in analysing and automatically processing large amounts of political data has increased in the past decades. |
| Approach: | They address the semi-automatic annotation of subjects in the Danish Parliament Corpus (2009-2017) v.2 and describe multi-label classification experiments to verify the consistency of the subject annotation. |
| Outcome: | The proposed method improves on the baseline classifier, which is a majority classifier. |
Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering (2021.acl-long)
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| Challenge: | Existing extractive summarization-based collaborative filtering models learn accurate representations of users and items based on user-given numeric ratings, but employing them is an oversimplification of user preferences and item characteristics. |
| Approach: | They propose to use BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddations, representation-explanations, and user-item interactions to create extractive summaries. |
| Outcome: | The proposed model improves rating prediction accuracy and user/item explainability. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
Sycophancy Hides Linearly in the Attention Heads (2026.eacl-long)
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Rifo Ahmad Genadi, Munachiso Samuel Nwadike, Nurdaulet Mukhituly, Tatsuya Hiraoka, Hilal AlQuabeh, Kentaro Inui
| Challenge: | Using TruthfulQA as the base dataset, we find that probes trained on TruthfulQ transfer effectively to other factual QA benchmarks. |
| Approach: | They train linear probes across the residual stream, multilayer perceptron, and attention layers to analyze where sycophancy signals emerge. |
| Outcome: | The proposed model can be used to steer truthfulness and toxicity behaviors. |
All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) perform well on a multitude of computational tasks, yet their inner workings remain unclear. |
| Approach: | They propose two techniques to inhibit input-specific token computations in initial layers . they propose a transformer that allows for any token to immediately access all preceding tokens . |
| Outcome: | The proposed algorithms can perform on a variety of mental math tasks with high accuracy and transfer across models. |