ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks (2024.acl-long)
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| Challenge: | Existing models overlook the temporal dimension in their training process, leading to suboptimal performance over time. |
| Approach: | They propose a training paradigm that trains models on chronological splits, preserving the temporal order of the data. |
| Outcome: | The proposed model fails to fit to recent data, despite continual learning and temporal invariant methods. |
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| Challenge: | a rapid evolution of legal concepts requires that legal language models adapt swiftly accounting for the temporal dynamics. |
| Approach: | They propose a dynamic mixture of experts model that explicitly models the temporal evolution of legal language in an online learning framework. |
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Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
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| Challenge: | Large-scale multi-label document classification presents interesting challenges due to the large label space and two-tiered skewed label distributions. |
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Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)
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| Challenge: | Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks. |
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Chronos: Learning Temporal Dynamics of Reasoning Chains for Test-Time Scaling (2026.findings-acl)
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| Challenge: | Existing methods for testing time scales treat reasoning traces or tokens equally, ignoring substantial variations in trajectory quality and localized logical failures. |
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GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
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Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2022.naacl-main)
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| Challenge: | a pretrained model is optionally adapted through domain-specific pretraining, followed by task-specific finetuning. |
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Embedding Time Expressions for Deep Temporal Ordering Models (P19-1)
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| Challenge: | Existing data-driven models fail to capture explicit temporal signals, such as dates and time windows. |
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TRANSIENTTABLES: Evaluating LLMs’ Reasoning on Temporally Evolving Semi-structured Tables (2025.naacl-long)
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| Challenge: | a recent study shows that large language models are limited in their ability to reason over time due to static datasets. |
| Approach: | They present a dataset that includes 3,971 questions derived from over 14,000 tables . they introduce a template-based question-generation pipeline that harnesses LLMs to refine questions . |
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ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs (2026.findings-eacl)
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| Challenge: | Unstructured data is expanding at an unprecedented rate, and static knowledge graphs are often overlooked due to their dynamic nature and lack of time-sensitive features. |
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