Editing Conceptual Knowledge for Large Language Models (2024)

Abstract: Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, while whether LLMs possess the capability to modify concepts remains unclear. This paper pioneers the investigation of editing conceptual knowledge for LLMs, by constructing a novel benchmark dataset ConceptEdit and establishing a suite of new metrics for evaluation. The experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge in LLMs, leading to poor performance. We anticipate this work can inspire further progress in understanding LLMs.

## introduction the emergence of large language models (llms) represents a significant step towards the era of agi, with the performance of large-scale models being evident for all to see @xcite . despite their advancements, llms encounter challenges such as misinformation, outdated knowledge due to the training cut-off, and the risk of producing toxic content @xcite @xcite @xcite . since retraining llms to address these issues is timeconsuming and costly, there is a surge necessity for advancements in knowledge editing methods designed for llms, which facilitate efficient, posttraining adjustments to the models @xcite @xcite @xcite @xcite . besides, sparse autoencoders could generate interpretable features for llms' behavior @xcite . recent knowledge editing methods can achieve the instance-level editing ability to alter knowledge in llms. yet, such a case-by-case setting of knowledge editing is highly inefficient and lacks modeling of relations between instances. cognitive science @xcite @xcite has revealed that humans understand new things and acquire new knowledge through learning concepts. for example, the concept camelidae is large, strictly herbivorous animals with slender necks and long legs. this abstraction, derived from concrete instances like llama and alpaca, assists in categorizing new entities. humans can achieve updates to a large amount of instances through concepts, thus, the llama does not belong to camelidae anymore. the distinctiveness of human leads to the research question: whether llms learn and update concepts analogously @xcite @xcite , as well as how to encapsulate and update concepts within parametric framework @xcite . to this end, we propose conceptedit foot_0 , a novel benchmark dataset for editing conceptual knowledge, which tries to modify the definition of concepts in llms. conceptedit is constructed upon the foundation of dbpedia ontology @xcite , a widely recognized and cross-domain ontology that preserves conceptual knowledge hierarchically. we build concepts with corresponding definitions and associated instances, accompanied by necessary elements for editing. except for the common metrics for instance-level editing, we design two concept-specific metrics, instance change for top-down influence on instances and concept consistency for semantic similarity of generated definition. experiments with ft, rome, memit, and prompt methods show that recent knowledge editing baselines can reach high reliability in distorting concept-level definitions for llms, but still perform poorly on concept-specific metrics. moreover, conceptual operation represents a higher dimension of pre-training models, distinct from the learning through demonstrations of individual instances. concept editing allows for efficient updates by generalizing from one to many or implementing controllable content generation through abstract expression interventions. it changes the model's understanding of abstract affairs through the manipulation of a concept, thereby achieving more efficient model control. in conclusion, our investigation leads to a collection of interesting findings, where we highlight the following contributions: ## background the objective of knowledge editing is to rectify particular factual inaccuracies encountered, with-out retraining the foundational model, while emphasizing the preservation of unrelated knowledge to the greatest extent, as elucidated by @xcite . the given edit descriptor (x, y) symbolizes the pairs that denote inputs and corresponding outputs embedded in llms. the base model f θ undergoes an extensive learning to assimilate the edited (x e , y e ), ultimately producing an edited model as f θe . to achieve this goal, x e and y e need to be concatenated to maximize the conditional probability, formally expressed as θ @xmath0; θ). at present, a burgeoning interest in exploring the capabilities of knowledge editing exists @xcite @xcite , with the goal of developing more advanced methodologies. these researches primarily concentrate on modifying factual knowledge typically at the instance level, encompassing various aspects. factual knowledge datasets, like zsre @xcite and coun-terfact @xcite , are frequently used as benchmarks @xcite @xcite . zsre, context-free questionanswering dataset, uses rephrasings generated by backtranslation as the equivalence neighborhood and train/val splits. for example, the answer of "which continent is mount andrewson?" is changed to "south america". counterfact identifies between superficial changes in model word selections from specific and generalized alterations. employing triples as an external knowledge repository through their conversion into natural language is favored because relational datasets offer more definitive query responses, enhancing convenience in evaluations. these can be proficiently integrated to see how changes affect instance-level facts. ## related work the current methods for knowledge editing are categorized into two main groups, those centered on preserving existing parameters and those entailing modification. the preservative methods incorporate explicit memory and prompting techniques to rectify model predictions. examples include serac @xcite , mem-prompt @xcite and ike @xcite . some modify the feed-forward neural network (ffn) layer, as exemplified by ca-linet @xcite ,t-patcher @xcite and grace @xcite . alternatively, the locate-and-edit approaches need to first locate the relevant neurons, followed by the adjustment of corresponding target parameters. representative studies are kn @xcite , rome @xcite , and memit @xcite . conversely, meta-learning utilize a hyper-network, a smaller network that generates the weights for layers in the main network, including ke @xcite , malmen @xcite and mend @xcite . to facilitate the research of knowledge editing, numerous datasets are exploring the potentialities and far-reaching effects. mquake @xcite challenges model updates to factual changes using multi-hop questions, while rippleedits @xcite and recoe @xcite expand the scope to encompass reasoning over subsequent facts. bake @xcite assesses the reversibility of editing. given the sequence of batch edits, @xcite identify paired edits that generate conflicts, and @xcite examine dependency within internal logical constraints. except for datasets which edit objects in triples, @xcite adopt a relation-centric perspective in edits. @xcite investigate how the edits impact the safety. to the best of our knowledge, we are the first benchmark in llms for conceptual knowledge editing. ## conclusion we introduce the conceptual knowledge editing task for llms, with a new benchmark conceptedit and evaluation metrics. from the experiments, we observe that existing editing methods, when modifying conceptual knowledge, have a very limited impact on the underlying instances; thus, stronger techniques and better understandings of concepts in llms are necessary for further research.