Papers with KE
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)
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| Challenge: | Existing language representation models (PLMs) cannot capture factual knowledge from text. |
| Approach: | They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM. |
| Outcome: | The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks. |
Retrieval-Augmented Multilingual Knowledge Editing (2024.acl-long)
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| Challenge: | Knowledge editing (KE) is an effective and economical alternative to inject new knowledge or to fix factual errors in Large Language Models (LLMs). |
| Approach: | They propose a multilingual knowledge editing method that can be used to update knowledge in LLMs by concatenating new knowledge retrieved from a knowledge base with users’ prompts before querying an LLM. |
| Outcome: | The proposed method outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. |
Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts (D19-55)
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| Challenge: | a new approach to knowledge extraction (KE) is needed for the health domain. |
| Approach: | They propose an approach to extracting knowledge about antidepressant drug nonadherence from health forums. |
| Outcome: | The proposed approach can be used to extract knowledge about antidepressant drug nonadherence from health forums. |
ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods face limited knowledge coverage in existing knowledge bases, infeasibility of annotating labels for an overabundance of commonsense knowledge, and strict knowledge formats. |
| Approach: | They propose a framework that integrates conceptualization and instantiation into the KE pipeline for LLMs to enhance their commonsense reasoning capabilities. |
| Outcome: | The proposed framework diagnoses implausible commonsense knowledge within an LLM and augments the source knowledge to be edited with conceptualization for stronger generalizability. |
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
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Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
ScEdit: Script-based Assessment of Knowledge Editing (2025.findings-acl)
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Xinye Li, Zunwen Zheng, Qian Zhang, Dekai Zhuang, Jiabao Kang, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui
| Challenge: | Knowledge Editing (KE) has gained increasing attention, yet current evaluation frameworks do not integrate KE into real-world application scenarios. |
| Approach: | They propose a script-based benchmark which encompasses both counterfactual and temporal edits and integrates token-level and text-level evaluation methods. |
| Outcome: | The proposed method combines token-level and text-level evaluation methods with a new fact-based evaluation framework. |
Are Your Keywords Like My Queries? A Corpus-Wide Evaluation of Keyword Extractors with Real Searches (2025.coling-main)
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| Challenge: | Keyword Extraction (KE) is essential in Natural Language Processing (NLP) for identifying key terms that represent the main themes of a text. |
| Approach: | They propose to use real query data from Google Trends to evaluate keywords extracted from a text to capture users' top queries. |
| Outcome: | The proposed method can be used with both supervised and unsupervised KE approaches and shows that KeyBERT is the most effective in capturing users’ top queries. |
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)
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| Challenge: | Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document. |
| Approach: | They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting. |
| Outcome: | The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately. |
SimCKP: Simple Contrastive Learning of Keyphrase Representations (2023.findings-emnlp)
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| Challenge: | Existing models for keyphrase generation and keyphrase extraction use a token level to generate keyphrases that do not appear in a document. |
| Approach: | They propose a simple contrastive learning framework that generates keyphrases that do not appear in a document and a reranker that adapts the scores for each generated phrase. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple benchmark datasets. |
AdaEdit: Advancing Continuous Knowledge Editing For Large Language Models (2025.acl-long)
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| Challenge: | Existing knowledge editing methods that can efficiently update knowledge in LLMs are limited due to budget constraints. |
| Approach: | They propose a method that can enhance the performance of edited LLMs in large-size continuous editing regimes. |
| Outcome: | Extensive empirical evaluations on multiple LLMs show that the proposed method outperforms existing methods without compromising the general abilities of these models. |
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing (2026.eacl-long)
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| Challenge: | Existing knowledge editing methods show promising results on general-domain benchmarks, but their effectiveness in the medical domain remains largely unexplored. |
| Approach: | They propose a framework to evaluate medical knowledge editing using model-generated rationales as editing targets. |
| Outcome: | The proposed method improves editing efficacy and generalization in medical models without full retraining. |
Empirical Study of Zero-shot Keyphrase Extraction with Large Language Models (2025.coling-main)
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| Challenge: | a prompting-based approach can effectively supersede traditional KE methods, a study shows . our code is available at https://github.com/kangnlp/zero-shot-keyphrase-extraction-with-LLMs. |
| Approach: | They propose four prompting strategies for zero-shot keyphrase extraction using Large Language Models. |
| Outcome: | The proposed prompting strategies outperform state-of-the-art prompting methods on KE benchmark datasets. |
CLICKER: Cross-Lingual Knowledge Editing via In-Context Learning with Adaptive Stepwise Reasoning (2026.findings-eacl)
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| Challenge: | Existing knowledge editing methods are static and fail to propagate edits across languages. |
| Approach: | They propose a KE method that dynamically retrieves only knowledge relevant to a given query and edits it to maintain cross-lingual consistency. |
| Outcome: | The proposed method outperforms static KE methods on a multilingual dataset with semantically similar but irrelevant prompts. |
Knowledge Editing in Language Models via Adapted Direct Preference Optimization (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) can become outdated over time due to lack of updated world knowledge. |
| Approach: | They propose to use weight updates to improve LLM alignment without retraining . they propose a method that continually updates the knowledge stored in the model . |
| Outcome: | The proposed method is more effective than existing methods on large datasets and models. |
EVEDIT: Event-based Knowledge Editing for Deterministic Knowledge Propagation (2024.emnlp-main)
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| Challenge: | Existing knowledge editing approaches only operate on (subject, relation, object) triple . current methods are limited to (substance, relation) triple, causing low confidence in their answers. |
| Approach: | They propose a task of event-based knowledge editing that pairs facts with event descriptions to improve model confidence. |
| Outcome: | The proposed method improves model confidence by 55.6% while maintaining the naturalness of generation. |
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. |
| Approach: | They propose to use Multi-hop Questioning Answering under Knowledge Editing for Arabic Language to update and/or edit prior knowledge and test it via Multi-Hop Question Answering (MQA). |
| Outcome: | The proposed model outperforms baseline models by a significant margin . it can be used to update and/or edit prior knowledge and then test it with MQA . |
BABELEDITS: A Benchmark and a Modular Approach for Robust Cross-lingual Knowledge Editing of Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for cross-lingual knowledge editing are limited in their effectiveness and robustness. |
| Approach: | They propose a new CKE benchmark that accounts for the rich variety of entity aliases within and across languages. |
| Outcome: | The proposed method is more effective than state-of-the-art methods and robust against model collapse when subjected to multiple edits. |
Context-Robust Knowledge Editing for Language Models (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods assess success by considering only edited knowledge without preceding contexts. |
| Approach: | They propose a method to strengthen context robustness by minimizing context-sensitive variance in hidden states of the model. |
| Outcome: | The proposed method improves the success rate in situations where a preceding context is present and preserves the overall capabilities of the model. |
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways. |
| Approach: | They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis. |
| Outcome: | The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory. |
Why Does New Knowledge Create Messy Ripple Effects in LLMs? (2024.emnlp-main)
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| Challenge: | Existing research has focused on post-training knowledge editing (KE) for language models to ensure that knowledge remains accurate and up-to-date. |
| Approach: | They propose to use a GradSim indicator to detect when and why updated knowledge ripples in language models. |
| Outcome: | The proposed indicator GradSim shows that LMs that fail to handle ripple effects have low GradSIM. |
SAKE: Steering Activations for Knowledge Editing (2025.acl-long)
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| Challenge: | Large Langue Models memorize facts, but they suffer from several limitations . Using a single input prompt is insufficient to capture the complexity of the knowledge scope affected by edits. |
| Approach: | They propose a steering activation method that models a fact to be edited as a distribution rather than a single prompt. |
| Outcome: | The proposed method can perform more robust edits than existing methods. |
Decoding by Contrasting Knowledge: Enhancing Large Language Model Confidence on Edited Facts (2025.acl-long)
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| Challenge: | In-context knowledge editing (ICE) is currently the most effective method for knowledge editing, but it is constrained by the black-box modeling of LLMs and lacks interpretability. |
| Approach: | They propose a method to decode new knowledge by comparing logits with unedited knowledge to improve the accuracy of LLMs. |
| Outcome: | The proposed method improves the performance of LLaMA3-8B-instruct on MQuAKE by up to 219%. |
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition (2025.findings-emnlp)
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| Challenge: | Existing methods for knowledge editing fail to work in multi-hop question answering due to 'edit skipping' edit skipping occurs due to the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. |
| Approach: | They propose a retrieval-augmented generation-based method that edits knowledge without modifying parameters without retraining LLMs. |
| Outcome: | The proposed method outperforms state-of-the-art methods for KE in multi-hop question answering. |
CAKE: Causal-Guided Adaptive Knowledge Editing for LLMs (2026.acl-long)
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| Challenge: | Existing knowledge editing methods rely on fixed-layer selection and uniform residual assignment, ignoring heterogeneous causal efficacy of different layers. |
| Approach: | They propose a method that allows for a causally-guided adaptive knowledge editing that combines causal tracing scores with a constrained quadratic optimization problem. |
| Outcome: | The proposed method achieves comparable performance with comparable overhead. |