통찰 - NLP, AI - # Explanations from Large Language Models

Faithfulness vs. Plausibility in Explanations from Large Language Models

Q: What implications does prioritizing plausibility over faithfulness have on decision-making in high-stakes scenarios?

In high-stakes scenarios such as healthcare, finance, and legal contexts, the implications of prioritizing plausibility over faithfulness can be severe. Plausible explanations generated by LLMs may appear logical and convincing to human users but might not accurately reflect the model's actual reasoning process. This discrepancy can lead to misplaced trust and over-reliance on potentially inaccurate information provided by the LLM. In critical domains like healthcare, relying on plausible but unfaithful explanations could result in incorrect diagnoses or treatment plans, ultimately harming patients' well-being. Furthermore, security concerns arise when plausible explanations mislead users into believing that certain safety measures are in place when they are not. For instance, if an LLM refuses to provide instructions for producing a harmful substance based on specific keywords in a prompt but later complies with similar requests under different contexts, it indicates that the explanation was more about heuristic filters than true understanding or safety protocols. Therefore, prioritizing plausibility over faithfulness in high-stakes scenarios can lead to erroneous decisions, compromised safety measures, and a lack of transparency regarding how models arrive at their conclusions.

Q: How can fine-tuning approaches improve the accuracy and transparency of self-explanations generated by LLMs?

Fine-tuning approaches play a crucial role in enhancing the accuracy and transparency of self-explanations generated by Large Language Models (LLMs). By training LLMs on domain-specific datasets relevant to high-stakes applications where faithful explanations are essential (such as healthcare diagnostics or legal advice), fine-tuning allows models to learn from curated data sets with aligned reasoning patterns. Through fine-tuning techniques tailored for specific application areas, LLMs can retain their broad knowledge while adapting their decision-making processes to generate more accurate and transparent explanations. Training models on high-quality datasets ensures that explanations align with correct reasoning patterns specific to each domain. Additionally, fine-tuning enables researchers to create tools that clinicians or experts within these fields can rely upon for understanding model decisions before implementing them in real-world scenarios. By focusing on domain-specific data sets during training sessions through fine-tuning methods tailored for various applications areas improves both accuracy and transparency of self-explanations provided by LLMs.

Q: In what ways can Mechanistic Interpretability help create more interpretable LLM models with enhanced faithfulness?

Mechanistic Interpretability offers a promising approach towards creating more interpretable Large Language Models (LLMs) with enhanced faithfulness. This method involves dissecting an LLM's internal workings to understand how its components interact in producing outputs effectively mapping specific neurons or neuron groups within the model's architecture back to aspects of its reasoning process. By developing methodologies that allow researchers to trace decision paths within an LLM back to individual components responsible for generating particular responses enhances interpretability significantly. Through Mechanistic Interpretability techniques researchers gain insights into how different parts of an LMM contribute towards generating outputs leading towards greater clarity around why certain decisions were made improving overall interpretability.

핵심 개념

Balancing faithfulness and plausibility in explanations from large language models is crucial for ensuring transparency and accuracy in high-stakes decision-making scenarios.

초록

Large Language Models (LLMs) generate self-explanations (SEs) that aim to elucidate their decision-making processes. While SEs are conversational and plausible, there is a lack of understanding regarding their faithfulness. The dichotomy between faithfulness and plausibility in SEs generated by LLMs raises concerns about the reliability of these explanations. Ensuring faithful explanations is critical for high-stakes applications like healthcare and legal advice. Current trends prioritize plausibility over faithfulness, driven by user-friendly interfaces, which may compromise the accuracy of explanations. Developing novel methods to enhance the faithfulness of SEs is essential for transparent deployment of LLMs in various settings.
Recent works highlight challenges faced by LLMs in generating self-explanations that accurately reflect their reasoning processes. Techniques like Chain-of-Thought reasoning, Token Importance, and Counterfactual Explanations offer unique insights into the model's decision-making process. Evaluating the faithfulness of explanations remains challenging due to the lack of ground truth explanations and the black-box nature of LLMs.
The community must focus on developing reliable metrics to characterize faithfulness and pioneering strategies to generate more faithful SEs. Fine-tuning approaches, In-Context Learning (ICL), and Mechanistic Interpretability (Mech Interp) are potential directions for enhancing the faithfulness of generated explanations.

통계

Recent works show that state-of-the-art LLMs generate plausible explanations but may not be factually grounded.
Turpin et al. simulate counterfactual inputs to measure unfaithfulness in self-explanations.
Lanham et al. use post-hoc reasoning as an indicator of faithfulness in explanations.
Chen et al. introduce metrics to assess counterfactual simulatability in LLM-generated explanations.

인용구

"Ensuring faithful explanations is critical for high-stakes applications like healthcare diagnostics, legal advice, and financial forecasting."
"Developing novel methods to enhance the faithfulness of self-explanations is essential for transparent deployment of LLMs."
"The community must focus on developing reliable metrics to characterize faithfulness and pioneering strategies to generate more faithful SEs."

핵심 통찰 요약

Faithfulness vs. Plausibility

by Chirag Agarw... 게시일 arxiv.org 03-15-2024

https://arxiv.org/pdf/2402.04614.pdf

더 깊은 질문

What implications does prioritizing plausibility over faithfulness have on decision-making in high-stakes scenarios?

In high-stakes scenarios such as healthcare, finance, and legal contexts, the implications of prioritizing plausibility over faithfulness can be severe. Plausible explanations generated by LLMs may appear logical and convincing to human users but might not accurately reflect the model's actual reasoning process. This discrepancy can lead to misplaced trust and over-reliance on potentially inaccurate information provided by the LLM. In critical domains like healthcare, relying on plausible but unfaithful explanations could result in incorrect diagnoses or treatment plans, ultimately harming patients' well-being.
Furthermore, security concerns arise when plausible explanations mislead users into believing that certain safety measures are in place when they are not. For instance, if an LLM refuses to provide instructions for producing a harmful substance based on specific keywords in a prompt but later complies with similar requests under different contexts, it indicates that the explanation was more about heuristic filters than true understanding or safety protocols.
Therefore, prioritizing plausibility over faithfulness in high-stakes scenarios can lead to erroneous decisions, compromised safety measures, and a lack of transparency regarding how models arrive at their conclusions.

How can fine-tuning approaches improve the accuracy and transparency of self-explanations generated by LLMs?

Fine-tuning approaches play a crucial role in enhancing the accuracy and transparency of self-explanations generated by Large Language Models (LLMs). By training LLMs on domain-specific datasets relevant to high-stakes applications where faithful explanations are essential (such as healthcare diagnostics or legal advice), fine-tuning allows models to learn from curated data sets with aligned reasoning patterns.
Through fine-tuning techniques tailored for specific application areas, LLMs can retain their broad knowledge while adapting their decision-making processes to generate more accurate and transparent explanations. Training models on high-quality datasets ensures that explanations align with correct reasoning patterns specific to each domain.
Additionally, fine-tuning enables researchers to create tools that clinicians or experts within these fields can rely upon for understanding model decisions before implementing them in real-world scenarios. By focusing on domain-specific data sets during training sessions through fine-tuning methods tailored for various applications areas improves both accuracy and transparency of self-explanations provided by LLMs.

In what ways can Mechanistic Interpretability help create more interpretable LLM models with enhanced faithfulness?

Mechanistic Interpretability offers a promising approach towards creating more interpretable Large Language Models (LLMs) with enhanced faithfulness. This method involves dissecting an LLM's internal workings to understand how its components interact in producing outputs effectively mapping specific neurons or neuron groups within the model's architecture back to aspects of its reasoning process.
By developing methodologies that allow researchers to trace decision paths within an LLM back to individual components responsible for generating particular responses enhances interpretability significantly. Through Mechanistic Interpretability techniques researchers gain insights into how different parts of an LMM contribute towards generating outputs leading towards greater clarity around why certain decisions were made improving overall interpretability.

Faithfulness vs. Plausibility in Explanations from Large Language Models