insight - Natural Language Processing - # Generative Language Models

Multi-Level Explanations for Generative Language Models: A Framework for Improved Understanding

Q: How do different scalarizers impact the interpretation of model outputs?

Scalarizers play a crucial role in translating text outputs from generative language models into real numbers for attribution analysis. Different scalarizers, such as Log Prob, BERT, Sim, SUMM, and BART, have varying impacts on the interpretation of model outputs. The choice of scalarizer can significantly influence the fidelity and relevance of the explanations provided by attribution algorithms. For example: Log Prob Scalarizer: This scalarizer uses log probabilities derived from model logits to quantify the sensitivity of input units in generating the output sequence. It provides a direct link between model predictions and explanation scores. BERT Score: Based on cosine similarity between embeddings for generated text and original output sequences, this scalarizer captures semantic similarities but may overlook specific nuances. SUMM Scalarizer: Specifically designed for summarization tasks, it focuses on faithfulness to original summaries but may not capture all aspects of importance. BART Score: Similar to Log Prob but using an auxiliary model (fBART) to measure faithfulness in generating target sequences. The choice of scalarizer affects how well attributions align with human intuition about what parts of input texts are most influential in generating specific outputs. Some scalarizers may be more suitable for certain tasks or models based on their ability to capture different aspects like semantics or faithfulness.

Q: How can user preferences influence the choice of attribution methods in real-world applications?

User preferences play a significant role in determining which attribution methods are most effective and acceptable in real-world applications. Factors that influence user preference include: Interpretability vs Accuracy: Users might prioritize interpretable explanations over highly accurate ones if they need to understand how models make decisions rather than just relying on them blindly. Granularity Level: Users might prefer attribution methods that provide explanations at a level they find most useful - whether at sentence-level, phrase-level, or word-level depending on their needs. Ease-of-use: User-friendly interfaces that present explanations clearly and intuitively can impact user preference towards certain attribution methods. Consistency with Intuition: Users tend to favor attribution methods that align with their own understanding or expectations about how inputs contribute to model outputs. In practical applications where end-users interact with AI systems daily (e.g., chatbots providing recommendations), selecting an appropriate attribution method involves considering user feedback during development phases and ensuring that explanations meet users' needs for transparency and trustworthiness.

Q: What implications does the use of linear-complexity algorithms have on model explainability?

The use of linear-complexity algorithms has several implications for enhancing model explainability: Scalability: Linear-scaling algorithms ensure efficient computation even with long inputs by limiting perturbations based on unit counts rather than exponentially increasing combinations. Cost-effectiveness: Algorithms with linear complexity reduce computational costs associated with querying large language models multiple times during explanation generation processes. Interpretation Ease: Linear-scaling algorithms simplify interpretation by providing clear insights into which input units contribute most significantly to output predictions without overwhelming users or analysts with excessive information. Multi-Level Explanations: Linear-complexity algorithms enable multi-level approaches where attributions progress from coarse levels (sentences) down to finer levels (phrases/words) systematically without exponential increases in computational resources required. By utilizing linearly scalable techniques like Local Shapley (L-SHAP) or constrained LIME variants within frameworks like MExGen, organizations can achieve more efficient yet comprehensive interpretability solutions for complex generative language models across various tasks like summarization and question answering while maintaining high fidelity local interpretations essential for decision-making processes.

Conceitos Básicos

Perturbation-based explanation methods extended to generative language models through the MExGen framework provide more locally faithful explanations of generated outputs.

Resumo

The content introduces the MExGen framework, focusing on perturbation-based explanations for generative language tasks. It addresses challenges related to text output and long text inputs, proposing solutions like scalarizers and multi-level attributions. The systematic evaluation shows improved local fidelity in explaining generated outputs compared to alternative methods.

Abstract: Introduces the MExGen framework for generative language models.
Introduction: Discusses perturbation-based explanation methods and their application to generative tasks.
Challenges: Addresses challenges of text output and long input texts in generative tasks.
Data Extraction: Includes key metrics such as Spearman correlation matrices and perturbation curves.
Comparison Between Explainers: Compares performance of MExGen with P-SHAP and CaptumLIME.
User Study: Describes a user study evaluating perception of different attribution methods.

Estatísticas

Perturbation-based explanation methods such as LIME and SHAP are commonly applied to text classification.
We propose a general framework called MExGen that can be instantiated with different attribution algorithms.
We conduct a systematic evaluation of perturbation-based attribution methods for summarization and question answering.

Citações

"We propose the MExGen framework to extend perturbation-based input attribution to generative language models."
"Our results indicate that even when using a mismatched scalarizer, MExGen C-LIME can outperform P-SHAP in local fidelity."

Principais Insights Extraídos De

Multi-Level Explanations for Generative Language Models

by Lucas Montei... às arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14459.pdf

Multi-Level Explanations for Generative Language Models

Perguntas Mais Profundas

How do different scalarizers impact the interpretation of model outputs?

Scalarizers play a crucial role in translating text outputs from generative language models into real numbers for attribution analysis. Different scalarizers, such as Log Prob, BERT, Sim, SUMM, and BART, have varying impacts on the interpretation of model outputs. The choice of scalarizer can significantly influence the fidelity and relevance of the explanations provided by attribution algorithms. For example:

Log Prob Scalarizer: This scalarizer uses log probabilities derived from model logits to quantify the sensitivity of input units in generating the output sequence. It provides a direct link between model predictions and explanation scores.
BERT Score: Based on cosine similarity between embeddings for generated text and original output sequences, this scalarizer captures semantic similarities but may overlook specific nuances.
SUMM Scalarizer: Specifically designed for summarization tasks, it focuses on faithfulness to original summaries but may not capture all aspects of importance.
BART Score: Similar to Log Prob but using an auxiliary model (fBART) to measure faithfulness in generating target sequences.
The choice of scalarizer affects how well attributions align with human intuition about what parts of input texts are most influential in generating specific outputs. Some scalarizers may be more suitable for certain tasks or models based on their ability to capture different aspects like semantics or faithfulness.

How can user preferences influence the choice of attribution methods in real-world applications?

User preferences play a significant role in determining which attribution methods are most effective and acceptable in real-world applications. Factors that influence user preference include:

Interpretability vs Accuracy: Users might prioritize interpretable explanations over highly accurate ones if they need to understand how models make decisions rather than just relying on them blindly.
Granularity Level: Users might prefer attribution methods that provide explanations at a level they find most useful - whether at sentence-level, phrase-level, or word-level depending on their needs.
Ease-of-use: User-friendly interfaces that present explanations clearly and intuitively can impact user preference towards certain attribution methods.
Consistency with Intuition: Users tend to favor attribution methods that align with their own understanding or expectations about how inputs contribute to model outputs.

In practical applications where end-users interact with AI systems daily (e.g., chatbots providing recommendations), selecting an appropriate attribution method involves considering user feedback during development phases and ensuring that explanations meet users' needs for transparency and trustworthiness.

What implications does the use of linear-complexity algorithms have on model explainability?

The use of linear-complexity algorithms has several implications for enhancing model explainability:

Scalability: Linear-scaling algorithms ensure efficient computation even with long inputs by limiting perturbations based on unit counts rather than exponentially increasing combinations.
Cost-effectiveness: Algorithms with linear complexity reduce computational costs associated with querying large language models multiple times during explanation generation processes.
Interpretation Ease: Linear-scaling algorithms simplify interpretation by providing clear insights into which input units contribute most significantly to output predictions without overwhelming users or analysts with excessive information.
Multi-Level Explanations: Linear-complexity algorithms enable multi-level approaches where attributions progress from coarse levels (sentences) down to finer levels (phrases/words) systematically without exponential increases in computational resources required.

By utilizing linearly scalable techniques like Local Shapley (L-SHAP) or constrained LIME variants within frameworks like MExGen, organizations can achieve more efficient yet comprehensive interpretability solutions for complex generative language models across various tasks like summarization and question answering while maintaining high fidelity local interpretations essential for decision-making processes.

Multi-Level Explanations for Generative Language Models: A Framework for Improved Understanding