COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for "knowledge editing" in large language models are inadequate . authors propose a method that can be used to update outdated information or correct false information . |
| Approach: | They propose a unified knowledge editing method called in-COntext retrieval-augmented Mass-Editing Memory . it incorporates retrieval augmented IKE, a novel extension of IKE designed for massive editing tasks . |
| Outcome: | The proposed method outperforms existing methods on the zsRE and CounterFact datasets. |
Similar Papers
DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | In-context knowledge editing has shown respectable abilities on knowledge editing in terms of generalization and specificity. |
| Approach: | They propose a novel extension of in-context knowledge editing (IKE) that allows for massive edits to be injected into large language models. |
| Outcome: | The proposed method shows state-of-the-art perfomrances and comparable performance with MIKE. |
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)
Copied to clipboard
Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang
| Challenge: | Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs). |
| Approach: | They propose to evaluate LLMs with single edit only and parameter-modifying ME with parameter-preserving ME. |
| Outcome: | The proposed method can maintain LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format. |
Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)
Copied to clipboard
| Challenge: | In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters. |
| Approach: | They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update. |
| Outcome: | The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects. |
Retrieval-Augmented Multilingual Knowledge Editing (2024.acl-long)
Copied to clipboard
| 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. |
In-Context Retrieval-Augmented Language Models (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing RALM methods focus on modifying the LM architecture to facilitate incorporation of external information, complicating deployment. |
| Approach: | They propose to condition a language model on relevant documents from a grounding corpus during generation by conditioning on external knowledge sources. |
| Outcome: | The proposed method significantly improves language modeling performance and provides natural source attribution mechanism. |
How to Make LLMs Forget: On Reversing In-Context Knowledge Edits (2025.naacl-long)
Copied to clipboard
| Challenge: | In-context knowledge editing (IKE) is an efficient and efficient knowledge editing method (Zheng et al., 2022b; Gangadhar and Stratos, 2024) it can be misused to manipulate responses opaquely, e.g., insert misinformation or offensive content. |
| Approach: | They propose to detect and reverse IKE-edits using only the top-10 output probabilities of the next token, even in a black-box setting. |
| Outcome: | The proposed method can be detected with high accuracy even in a black-box setting, achieving over 80% accuracy in recovering original, unedited outputs across multiple LLMs. |
Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that the performance of large language models is insufficient for non-English data, such as Korean. |
| Approach: | They propose a framework that integrates evidential information from external sources into the prompt for the Korean GEC task. |
| Outcome: | The proposed framework extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
Copied to clipboard
Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
CoME: An Unlearning-based Approach to Conflict-free Model Editing (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability. |
| Approach: | They propose a conflict-free model editing framework that selectively removes outdated knowledge from LLMs to improve their accuracy and reliability. |
| Outcome: | The proposed framework improves both editing accuracy and model reliability when applied to existing editing methods. |
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
| Approach: | They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs. |
| Outcome: | Experiments show that the proposed approach performs better than previous approaches on various benchmarks. |