| Challenge: | Existing studies have only considered language models as knowledge bases in a static setting . memorizing conflicting information is still challenging for LMs and hinders memorization of other unrelated one-to-one relationships. |
| Approach: | They propose two requirements for treating language models as temporal knowledge bases . they propose a dataset which is aimed at probing temporally-scoped knowledge . |
| Outcome: | The proposed model can store conflicting information and use stored knowledge for temporal knowledge queries. |
Similar Papers
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2021.eacl-main)
Copied to clipboard
| Challenge: | Pretrained language models have been suggested as an alternative or complement to structured knowledge bases . however, this paradigm has only been considered in a very limited setting . |
| Approach: | They propose a paradigm that allows LMs to store a large number of entities . they propose LM-as-KB paradigm which allows querying stored facts . |
| Outcome: | The proposed paradigm allows handling 21k entities whose name is found in common LM vocabularies . the proposed paradigm has only been considered in a very limited setting . |
Time-Aware Language Models as Temporal Knowledge Bases (2022.tacl-1)
Copied to clipboard
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, William W. Cohen
| Challenge: | Existing language models are trained on snapshots of data collected at a specific moment in time. |
| Approach: | They propose a diagnostic dataset aimed at probing LMs for factual knowledge that changes over time. |
| Outcome: | The proposed method improves memorization of seen facts and calibration on unseen facts from future time periods. |
Relational World Knowledge Representation in Contextual Language Models: A Review (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge bases are organized according to manual schemas that limit their expressiveness and require significant human engineering and maintenance. |
| Approach: | They propose to organize knowledge representation strategies in LMs by the level of KB supervision provided . they propose to highlight notable models, evaluation tasks, and findings . |
| Outcome: | The proposed model can internalize and express relational knowledge in more flexible forms. |
Are Large Language Model Temporally Grounded? (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent large language models lack a consistent temporal model of textual narratives . sentence ordering in unlabelled texts is only weakly correlated with event ordering . |
| Approach: | They evaluate LLMs with textual narratives and evaluate their common-sense knowledge . they find that LLM models struggle the most with self-consistency . |
| Outcome: | The proposed models lack a consistent temporal model of textual narratives. |
Can LMs Store and Retrieve 1-to-N Relational Knowledge? (2023.acl-srw)
Copied to clipboard
| Challenge: | Pretraining language models on large amounts of text has made it difficult to store and retrieve world knowledge. |
| Approach: | They propose to view pretrained language models as knowledge bases by examining their ability to store and retrieve world knowledge. |
| Outcome: | The proposed model can store and retrieve world knowledge with high accuracy, but it is not clear how accurately it can handle 1-to-N relational knowledge. |
Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to effectively retain and reason about temporal information remains limited. |
| Approach: | They propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. |
| Outcome: | The proposed methods improve performance and reduce incorrect outputs. |
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
Copied to clipboard
Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on LMs lack systematic studies on their structured reasoning capabilities over the infused knowledge. |
| Approach: | They investigate how LMs of different sizes can store world knowledge of different frequencies in a large-scale KB after training on the abundant world knowledge triplets. |
| Outcome: | The proposed models can store and respond to natural language queries with flexibility and reasoning abilities, but they need to be enhanced to fully realize their potential. |
TRANSIENTTABLES: Evaluating LLMs’ Reasoning on Temporally Evolving Semi-structured Tables (2025.naacl-long)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model improves on the TRANSIENTTABLES dataset . it demonstrates that the model can reason over time, even when it is not static . |
Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
| Approach: | They propose to use 3 prompting strategies to evaluate 8 different LLMs across 6 datasets and 2 Code Generation LMs to perform the analysis. |
| Outcome: | The proposed models perform better on NLP tasks than the standard models on the same dataset. |