Challenge: Shapley Values are often estimated with a small number of stochastic model evaluations, but this can only be mitigated by aggregating thousands of model evaluation.
Approach: They propose to combine a model with thousands of model evaluations to estimate Shapley Values without additional model evaluation.
Outcome: The proposed model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods and does not suffer from stability issues as inference is deterministic.

Similar Papers

SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)

Copied to clipboard

Challenge: Existing models with opacity problems have been proposed to address this problem.
Approach: They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values.
Outcome: The proposed framework is based on the Shapley-value-based model explanations.
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction (2021.findings-acl)

Copied to clipboard

Challenge: Neural network algorithms excel on Automatic Text Classification tasks, but they are expensive and require high computational costs.
Approach: They propose to exploit the cost-effectiveness of stacking of automatic text classification classifiers to improve their effectiveness.
Outcome: The proposed method can predict the best ensemble in each scenario using only fraction of available training data.
TokenShapley: Token Level Context Attribution with Shapley Value (2025.findings-acl)

Copied to clipboard

Challenge: Large language models (LLMs) have strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge.
Approach: They propose a token-level attribution method that combines Shapley value-based data attribution with KNN-based retrieval techniques to improve attribution accuracy.
Outcome: TokenShapley outperforms state-of-the-art methods on four benchmarks . it achieves an 11–23% improvement in accuracy on the benchmarks.
Robust Text Classifier on Test-Time Budgets (D19-1)

Copied to clipboard

Challenge: Recent advances in deep neural networks (DNNs) achieve high accuracy on many text classification tasks.
Approach: They propose a generic framework for learning a robust text classification model . they use a data aggregation method to train the classifier on a large corpus of text .
Outcome: The proposed framework achieves consistent speedup with little degradation in accuracy on four benchmark text classification tasks.
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)

Copied to clipboard

Challenge: Existing methods to reduce model's reliance on bias features ignore the learnability of these features.
Approach: They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features.
Outcome: The proposed models can perform better on out-of-distribution datasets than baseline models with a more sophisticated model design.
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)

Copied to clipboard

Challenge: Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources .
Approach: They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks.
Outcome: The proposed method reduces the size of the extracted representations by a factor of 16 to store them for later use.
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

Copied to clipboard

Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
Approach: They propose to use different types of model architectures to improve extractive summarization systems.
Outcome: The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis.
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)

Copied to clipboard

Challenge: Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings.
Approach: They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models.
Outcome: The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings.
Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)

Copied to clipboard

Challenge: a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction .
Approach: They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data.
Outcome: The proposed methods improve utterance classification results by drawing on utterant variation.
Smaller Text Classifiers with Discriminative Cluster Embeddings (N18-2)

Copied to clipboard

Challenge: Word embeddings dominate overall model sizes in neural methods for natural language processing, especially when large vocabularies and high dimensions are used.
Approach: They propose a Gumbel-Softmax distribution to maximize over the latent clustering while minimizing the task loss.
Outcome: The proposed method minimizes the task loss while maximizing over the latent clustering while remaining parameter-efficient.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations