LIME-LLM: Probing Models with Fluent Counterfactuals, Not Broken Text

TL;DR

LIME-LLM introduces a hypothesis-driven perturbation framework for NLP explanations, replacing random noise with fluent, on-manifold neighborhoods to improve local explanation fidelity.

Contribution

It proposes a novel controlled perturbation method using LLMs that isolates feature effects more effectively than existing heuristic or generative approaches.

Findings

Outperforms baseline explanation methods in fidelity across multiple benchmarks.

Produces fluent, semantically valid neighborhood samples for better interpretability.

Establishes new state-of-the-art in NLP local explanation fidelity.

Abstract

Local explanation methods such as LIME (Ribeiro et al., 2016) remain fundamental to trustworthy AI, yet their application to NLP is limited by a reliance on random token masking. These heuristic perturbations frequently generate semantically invalid, out-of-distribution inputs that weaken the fidelity of local surrogate models. While recent generative approaches such as LLiMe (Angiulli et al., 2025b) attempt to mitigate this by employing Large Language Models for neighborhood generation, they rely on unconstrained paraphrasing that introduces confounding variables, making it difficult to isolate specific feature contributions. We introduce LIME-LLM, a framework that replaces random noise with hypothesis-driven, controlled perturbations. By enforcing a strict "Single Mask-Single Sample" protocol and employing distinct neutral infill and boundary infill strategies, LIME-LLM constructs fluent, on-manifold neighborhoods that rigorously isolate feature effects. We evaluate our method against established baselines (LIME, SHAP, Integrated Gradients) and the generative LLiMe baseline across three diverse benchmarks: CoLA, SST-2, and HateXplain using human-annotated rationales as ground truth. Empirical results demonstrate that LIME-LLM establishes a new benchmark for black-box NLP explainability, achieving significant improvements in local explanation fidelity compared to both traditional perturbation-based methods and recent generative alternatives.