ER-Test: Evaluating Explanation Regularization Methods for Language Models

Joshi, Brihi; Chan, Alvin T. S.; Liu, Ziyi; Nie, Shaoliang; Sanjabi, Maziar; Firooz, Hamed; Ren, Xiang

doi:10.18653/v1/2022.findings-emnlp.242

Cited by 2 publications

(1 citation statement)

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“…Furthermore, hARs can teach ML models "valid reasons" for a classification, reducing spurious ML model behavior (Mathew et al, 2021;Chen et al, 2022;Joshi et al, 2022) and improving out-of-domain (OOD) performance (Lu et al, 2022).…”

Section: Collection Aims and Benefitsmentioning

confidence: 99%

Human-annotated rationales and explainable text classification: a survey

Herrewijnen,

Nguyen,

Bex

et al. 2024

Front. Artif. Intell.

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Asking annotators to explain “why” they labeled an instance yields annotator rationales: natural language explanations that provide reasons for classifications. In this work, we survey the collection and use of annotator rationales. Human-annotated rationales can improve data quality and form a valuable resource for improving machine learning models. Moreover, human-annotated rationales can inspire the construction and evaluation of model-annotated rationales, which can play an important role in explainable artificial intelligence.

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Section: Collection Aims and Benefitsmentioning

confidence: 99%

Human-annotated rationales and explainable text classification: a survey

Herrewijnen,

Nguyen,

Bex

et al. 2024

Front. Artif. Intell.

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Explainability for Large Language Models: A Survey

Zhao,

Chen,

Yang

et al. 2024

ACM Trans. Intell. Syst. Technol.

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Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional deep learning models.

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ER-Test: Evaluating Explanation Regularization Methods for Language Models

Cited by 2 publications

References 0 publications

Human-annotated rationales and explainable text classification: a survey

Human-annotated rationales and explainable text classification: a survey

Explainability for Large Language Models: A Survey

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