Papers with CTM
Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties (2026.eacl-short)
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| Challenge: | Existing temporal reasoning benchmarks rely on rule-based construction and lack contextual depth . a recent study found existing LLMs struggle with nuanced temporal understanding . |
| Approach: | a benchmark is designed to evaluate LLMs on temporal reasoning in Chinese dynasties. |
| Outcome: | a new benchmark evaluates LLMs on temporal reasoning across Chinese dynasties . it emphasizes cross-entity relationships, pairwise temporal alignment, contextualized and culturally-grounded reasoning . results show existing LLM benchmarks struggle with nuanced temporal understanding . |
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling (2023.eacl-main)
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| Challenge: | Dropout is a regularization trick used to resolve overfitting in large feedforward neural networks, but there is nil analysis of it for unsupervised models and in particular, VAE-based neural topic models. |
| Approach: | They propose to use dropout to solve overfitting problems in unsupervised neural topic models by stochastically dropping out the activation of neurons to prevent complex co-adaptations of feature vectors. |
| Outcome: | The proposed class of neural topic models can be used to improve the quality and predictive performance of the generated topics. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
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| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
RoBERT2VecTM: A Novel Approach for Topic Extraction in Islamic Studies (2024.findings-emnlp)
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| Challenge: | a new approach to investigate “Hadith” texts presents challenges due to the complexity of Arabic . a novel neural-based approach to analyze “Matn” topics outperforms traditional NLP models . |
| Approach: | They propose a novel approach to analyze Arabic “Hadith” texts using the Contextualized Topic Model. |
| Outcome: | The proposed approach outperforms state-of-the-art models by generating more coherent topics in Arabic. |
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation (D19-1)
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| Challenge: | interacting with a model for Visual Question Answering (VQA) quickly reveals that these models lack consistency. |
| Approach: | They propose a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. |
| Outcome: | The proposed data augmentation module improves the consistency of VQA models on the Con-VQA dataset and is a strong baseline for future research. |
Pragmatic Norms Are All You Need – Why The Symbol Grounding Problem Does Not Apply to LLMs (2024.emnlp-main)
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| Challenge: | 'Symbol grounding problem' is a philosophical problem that arises when questionable theories of meaning are presupposed. |
| Approach: | They argue that LLMs are vulnerable to Harnad’s symbol grounding problem (SGP), as it has been claimed recently . they trace the origins of the SGP to the computational theory of mind . |
| Outcome: | The proposed model-theoretic semantics does not give rise to the SGP, as it has been claimed in the literature. |