Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness

1 Introduction

• LLMs offer factual recall with minimal human supervision and natural-language querying, unlike manually populated knowledge bases with syntax-specific queries.These advantages motivate their use as knowledge bases.Source: §1 · p. 1
• Editing LLMs matters because training data may contain inaccurate or outdated information, making their knowledge in need of correction.The stated goal is to help knowledge in LLMs reflect current real-world knowledge.Source: §1 · p. 1
• Dependency of knowledge comprises specificity—editing facts and lexical variations while preserving irrelevant facts—and implication awareness—deriving implications through If-Then rules.The paper illustrates implication awareness with an edited Apple CEO fact that changes an employer implication.Source: §1 · pp. 1–2
• Prior LLM editing work addressed specificity but neglected implication awareness, while interconnected learned knowledge makes an edit’s effects difficult to predict [Meng et al., 2022b; Mitchell et al., 2021; De Cao et al., 2021].The paper situates dependency research in LLMs-as-KBs as less mature than in knowledge bases [Fagin et al., 1983; Fan, 2008; Pawlak; Ullman, 1988; Arenas et al., 1999; Calı et al., 2003; Bohannon et al., 2006].Source: §1 · p. 2
• The establish-and-update protocol and DepEdit dataset evaluate specificity and implication awareness using controlled entities, relations, and If-Then rules.The method compares expected with actual edits in knowledge-editing simulations.Source: §1 · p. 2
• Existing methods are influenced by surface patterns such as question wording and struggle to infer implications, with gradient analysis showing difficulty identifying suitable parameter-update directions.The authors also identify potential in adapting knowledge-base dependency methods for editing LLM knowledge.Source: §1 · p. 2

2 Related Work

• Prior LLM editing methods use external learned editors to modify tuning gradients while keeping the base model unchanged, or manually edit activations in neurons or modules linked to target facts (De Cao et al., 2021; Mitchell et al., 2021; Meng et al., 2022a; Dai et al., 2022; Meng et al., 2022b).Source: §2 · p. 2
• Other work evaluates entailed facts, but focuses mainly on single-hop reasoning and lacks If-Then rules for tracking implication edits [Hase et al., 2023; Cohen et al., 2023a; Onoe et al., 2023].The paper’s testbed instead assesses how editing influences knowledge dependency.Source: §2 · p. 2
• Knowledge-base dependency methods build on principles including syntax irrelevance, persistence of prior knowledge, and primacy of new information.These principles address how meaning, existing information, and new information are handled during edits.Source: §2 · p. 3
• The paper maps syntax irrelevance and persistence of prior knowledge to specificity, assessing edits to facts and lexical variations while preserving irrelevant facts.The passage frames this mapping as an evaluation of editing methods in LLMs.Source: §2 · p. 3
• It maps primacy of new information to implication awareness, evaluating whether methods infer logical implications; prior LLM editing evaluations mainly omitted this constraint.The authors state that they are the first, to their knowledge, to establish this analogy and evaluate LLMs using these principles.Source: §2 · p. 3

3.1 Notations

• The protocol represents facts as triplets mapped to question-answer pairs and combines specific and unrelated facts with If-Then rules to derive implications.Rules join two premises with a logical AND; premises and implications can be expressed as templates or instantiated facts.Source: §3.1 · pp. 3–4

3.2 Overview

• In a controlled simulation, an establish phase records what a model learns from a knowledge set before an update phase edits selected facts.The protocol uses question answering as its base task, though the authors state it could also apply to other tasks.Source: §3.2 · p. 4

3.3 Establish Phase

• The establish phase records model predictions and questions as established knowledge, covering recalled specific facts, unrelated facts, and implications.The model may use arbitrary methods, including doing nothing, to establish the supplied knowledge.Source: §3.3 · p. 4

3.4 Update Phase

• The update phase edits selected specific facts and their implications in copies of the established model while checking that non-updated knowledge remains unchanged.The protocol tests lexical variations of edited facts and implications derived from the updated facts.Source: §3.4 · p. 4

3.5 Evaluation Metrics

• Exact-match metrics assess establishment, edits to facts and implications, and consistency of non-updated facts, implications, and unrelated facts.The metrics distinguish success on updated implications from consistency on implications that were not updated.Source: §3.5 · pp. 4–5

4 New Knowledge Editing Dataset: DepEdit

• DepEdit links question-answer pairs to triplets and includes If-Then rules, enabling evaluation of edits for specificity and implication awareness.The dataset is built using zsRE facts and rules mined from WikiData relations and filtered for plausibility.Source: §4 · p. 5

4.1 Dataset Curation

• Curation finds shared-entity relation cliques in WikiData from zsRE relation pairs, then crowdsources questions and relation templates.The templates replace entities with their types to express relations in natural language.Source: §4.1 · p. 5
• Curators generated three If-Then rules per clique, about 7,000 total, and retained rules both annotators rated must or likely true.Rules need not be logical entailments, but were intended to be plausible in a deployment setting.Source: §4.1 · p. 6

4.2 Crowdsourcing Tasks

• Crowdsourcing workers were recruited from English-speaking countries, needed over 95% approval and a passed qualification test, and received about 12–20 USD per hour.Each task took 20–40 seconds on average.Source: §4.2 · p. 6

4.3 Key Statics on DepEdit

• DepEdit contains 159,264 question-answer pairs, 530 If-Then rules, and 1,106 knowledge sets.Each knowledge set is constructed using the question-answer pairs and rules.Source: §4.3 · p. 6

5.1 Task Setup

• Simulations use knowledge sets with 29 facts, one If-Then rule, and 12 implications; 20 randomly selected facts are edited during the update phase.The setup divides facts into 24 specific and five unrelated facts.Source: §5.1 · p. 6
• CQ settings reuse question templates between editing and evaluation, while ICQ changes them; UT uses uniform templates and DT uses diverse templates.ICQ tests consistency across linguistic presentations, while UT tests discrimination between knowledge sharing similar surface forms.Source: §5.1 · pp. 6–7

5.2 Comparison Methods

• The comparison includes BART and GPT-XL baselines, FINETUNE, MEND, MEMIT, and a RANDOM baseline.GPT-XL is fine-tuned on a closed-book QA dataset to mitigate domain disparity.Source: §5.2 · p. 7

5.3.1 Specificity of editing

• MEMIT exceeds MEND in establish and update success, but performs worse than FINETUNE on consistency of non-updated specific facts.MEND and MEMIT outperform FINETUNE on consistency of unrelated facts.Source: Table 3 · p. 7, §5.3.1 · p. 7

5.3.2 Implication awareness of editing

• MEND and MEMIT achieve low implication success and treat implications like unrelated knowledge; adding If-Then rules to FINETUNE does not improve its establish or update success.The authors infer that language models do not internalize If-Then rules expressed in natural language.Source: Table 4 · p. 8, §5.3.2 · p. 8

6 Analysis

• The analysis separates implication-aware editing into finding an implication-update direction conditioned on premise edits and jointly updating facts and implications.The authors examine the joint-update sub-goal first because they consider its difficulty easier to estimate directly.Source: §6 · p. 8

6.1 Gradient Similarity between Updating Premise Facts and Implications

• Premise–implication gradient similarity is on par with similar-surface-form fact pairs, suggesting comparable update difficulty.The analysis also compares lexical-variation and randomly sampled fact pairs.Source: §6.1 · p. 8, Table 5 · p. 8

6.2 Gradient Direction to Update Implications

• MENDIMP performs poorly despite training on logical dependencies, while BARTDERIVE performs much better when premises are included in the prompt.This contrast suggests that predicting implications from given premises is not challenging when trained in that setup.Source: §6.2 · pp. 8–9, Table 6 · p. 8

6.3 Dependency Resolution with ForwChain and BackChain

• External inference improves implication-editing accuracy, but FORWCHAIN and BACKCHAIN can reduce consistency of non-updated implications.BACKCHAIN can compound inconsistencies across premise questions; FORWCHAIN requires updating more premise facts, making irrelevant knowledge easier to break.Source: §6.3 · p. 9, Table 7 · p. 9

• The paper introduces an evaluation protocol for specificity and implication awareness, implemented with DepEdit, a QA dataset containing natural-language If-Then rules.The protocol assesses editing methods for both forms of knowledge dependency.Source: §7 · p. 9
• State-of-the-art editing methods have lower success rates than vanilla fine-tuning and are highly influenced by the precise wording of facts.Source: §7 · p. 9
• Current methods show limited performance deriving logical consequences from facts using natural-language rules; future work could draw on knowledge-base editing methods.Source: §7 · p. 9

• DepEdit does not support complex logical operations such as quantification or probabilistic entailment.Source: §8 · p. 9
• Each knowledge set contains only one rule, and edit targets are allocated to avoid conflicts between facts and derived implications.The authors note such conflicts may occur in real-world applications.Source: §8 · p. 9
• The authors suggest extending DepEdit to combine multiple rules in a knowledge set or assign probabilities to rules and facts.Source: §8 · p. 9

• Many QA pairs are transformed from WikiData triplets, which may be inaccurate, and If-Then rules were labeled by a small group of annotators.Source: §9 · p. 9
• The facts and rules are not guaranteed to be factual and may reflect stereotypes or biases, so DepEdit is for testing computational models rather than fact-checking or decision-making.Source: §9 · p. 10

A.1 Mining Cliques of Relations and Question Collection

• More than 700 crowd-written QA pairs for about 150 new relations remained after roughly 900 pairs were collected and low-quality submissions filtered.The relations were mined from overlapping-entity triplets and relation triangles.Source: §A.1 · p. 12

A.2 Question-Answer pairs summarization and Potential Rules Construction

• Workers generalized same-relation QA pairs into descriptive entity-class templates, which were combined across relation triangles into about 7k potential If-Then rules.The rules contain two premises and one implication.Source: §A.2 · p. 12

A.3 Evaluation of plausibility of rules

• About 530 rules were retained after two-worker labeling and a second round requiring positive labels with perfect agreement.Workers judged whether the reasoning step was plausible, rather than whether the premises or conclusion were plausible on their own.Source: §A.3 · pp. 12–13

A.4 Other annotation details

• Annotation used worker qualification tests and blocked low-quality contributors; the authors report following institutional research-ethics procedures and plan academic release of DepEdit.The first author completed human-research ethics training.Source: §A.4 · p. 13

A.5 More examples in DepEdit

• Additional DepEdit knowledge-set examples are referenced in Table 8.Source: §A.5 · p. 13

B Experiment Details

• MEND used a 110M-parameter BART-based model and, due to multi-step editing instability, was evaluated on FINETUNE-established base models; MEMIT used GPT-XL.FINETUNE used RMSProp at 1e −5 for up to 50 steps, while MEND used Adam at 1r − 6; each method evaluation took under 8 GPU hours.Source: B Experiment Details · p. 13

• Vanilla MEND refines the gradient for a specific fact’s answer, with lexical variations and unrelated facts also specified in the setup.Source: C Details of Experiment Setup in… · p. 13
• MENDIMP’s learning objective uses implication question-answer pairs derived from specific facts and applicable rules.Source: C Details of Experiment Setup in… · p. 13
• Train, validation, and test sets share rules but use different entity pairs; each evaluation uses six facts and three implications, with four facts and two implications updated.This design focuses evaluation on implication awareness while mitigating the impact of limited specificity when editing large-scale facts.Source: C Details of Experiment Setup in… · p. 13