MaNtLE: Model-agnostic Natural Language Explainer

• URL: http://arxiv.org/abs/2305.12995v1
• Date: Mon, 22 May 2023 12:58:06 GMT
• Title: MaNtLE: Model-agnostic Natural Language Explainer
• Authors: Rakesh R. Menon, Kerem Zaman, Shashank Srivastava
• Abstract summary: We introduce MaNtLE, a model-agnostic natural language explainer that analyzes multiple classifier predictions. MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations. Simulated user studies indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations.
• Score: 9.43206883360088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches, such as LIME, generate algorithmic explanations by attributing importance to input features for individual examples, recent research indicates that practitioners prefer examining language explanations that explain sub-groups of examples. In this paper, we introduce MaNtLE, a model-agnostic natural language explainer that analyzes multiple classifier predictions and generates faithful natural language explanations of classifier rationale for structured classification tasks. MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations. Simulated user studies indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations across three tasks. Human evaluations demonstrate that users can better predict model behavior using explanations from MaNtLE compared to other techniques

Related papers

• XForecast: Evaluating Natural Language Explanations for Time Series Forecasting [72.57427992446698]
  Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions. Traditional explainable AI (XAI) methods, which underline feature or temporal importance, often require expert knowledge. evaluating forecast NLEs is difficult due to the complex causal relationships in time series data.
  arXiv  Detail & Related papers  (2024-10-18T05:16:39Z)
• Selective Explanations [14.312717332216073]
  A machine learning model is trained to predict feature attribution scores with only one inference. Despite their efficiency, amortized explainers can produce inaccurate predictions and misleading explanations. We propose selective explanations, a novel feature attribution method that detects when amortized explainers generate low-quality explanations.
  arXiv  Detail & Related papers  (2024-05-29T23:08:31Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• Understanding Post-hoc Explainers: The Case of Anchors [6.681943980068051]
  We present a theoretical analysis of a rule-based interpretability method that highlights a small set of words to explain a text's decision. After formalizing its algorithm and providing useful insights, we demonstrate mathematically that Anchors produces meaningful results.
  arXiv  Detail & Related papers  (2023-03-15T17:56:34Z)
• Explanations from Large Language Models Make Small Reasoners Better [61.991772773700006]
  We show that our method can consistently and significantly outperform finetuning baselines across different settings. As a side benefit, human evaluation shows that our method can generate high-quality explanations to justify its predictions.
  arXiv  Detail & Related papers  (2022-10-13T04:50:02Z)
• Interpreting Language Models with Contrastive Explanations [99.7035899290924]
  Language models must consider various features to predict a token, such as its part of speech, number, tense, or semantics. Existing explanation methods conflate evidence for all these features into a single explanation, which is less interpretable for human understanding. We show that contrastive explanations are quantifiably better than non-contrastive explanations in verifying major grammatical phenomena.
  arXiv  Detail & Related papers  (2022-02-21T18:32:24Z)
• Towards Interpretable Natural Language Understanding with Explanations as Latent Variables [146.83882632854485]
  We develop a framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model.
  arXiv  Detail & Related papers  (2020-10-24T02:05:56Z)
• Explanations of Black-Box Model Predictions by Contextual Importance and Utility [1.7188280334580195]
  We present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation.
  arXiv  Detail & Related papers  (2020-05-30T06:49:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.