Evaluating the Trustworthiness of a Generative Large Language Model using an Oracle-Checker Scheme

Core Concepts

This work presents a novel oracle-checker scheme for evaluating the trustworthiness of the output from a generative large language model (LLM) by designing specialized checkers that can validate the model's responses.

Abstract

This paper introduces an oracle-checker scheme for evaluating the trustworthiness of a generative large language model (LLM). The scheme involves two key components:

Oracle: The generative LLM, such as GPT-3.5, is treated as an "oracle" that can provide answers or responses for certain tasks.

Checker: The checker is designed to validate the oracle's responses based on specific strategies, including:
a. Property strategy: The checker verifies if the oracle's response satisfies certain properties, such as linear complexity for entity extraction.
b. Proof strategy: The checker tries to construct a proof that the oracle's "yes" response for semantic equivalence is valid.
c. Trust strategy: The checker verifies the truthfulness of the oracle's "no" response for semantic equivalence by finding contradictory evidence.

The authors demonstrate the implementation of these checker strategies in two separate contexts: entity extraction and paraphrase decision.
For entity extraction, the linearity checker based on the property strategy is used to validate the trustworthiness of the entities extracted by the LLM. The experiments show that the LLM's entity extraction is not always linear, indicating potential issues with its trustworthiness.
For paraphrase decision, the proof-based checker is used to validate the LLM's "yes" responses, while the trust-based checker is used to validate the "no" responses. The results suggest that the checker can effectively identify a subset of the LLM's responses that are trustworthy, even when the LLM disagrees with the ground truth labels.
The key contribution of this work is the novel oracle-checker scheme, which provides a framework for assessing the trustworthiness of generative LLMs in a more systematic and customizable manner, compared to existing approaches that rely on benchmarking or self-consistency.

Stats

The linearity test on entity extraction was performed on 5000 sentences from the DOCRED dataset and 500 sentences from the RISC-V specification.
The paraphrase decision experiments were conducted on 5000 sentence pairs from the MSR Paraphrase corpus.

Quotes

"Automatically validating an answer given by a LLM can be an intriguing problem. This is especially the case when using a labeled dataset for defining the function f is not sufficient."
"Under the property strategy, the person believes that the computation of f should satisfy a certain property. For example, f should follow a certain form of linear complexity."
"Under the proof strategy, the person accepts an answer if a proof can be constructed. Constructing a proof may require further interactions with the oracle."
"Under the trust strategy, the person accepts an answer if the oracle passes a type of truthfulness test. In this case, the person has no idea how the oracle computes f, or having any proof on the correctness of the answer."

Key Insights Distilled From

Oracle-Checker Scheme for Evaluating a Generative Large Language Model

by Yueling Jenn... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03170.pdf

Deeper Inquiries

How can the oracle-checker scheme be extended to handle more complex tasks beyond entity extraction and paraphrase decision?

The oracle-checker scheme can be extended to handle more complex tasks by adapting the checking strategies to suit the specific requirements of the task at hand. For tasks beyond entity extraction and paraphrase decision, the key lies in designing checkers that can effectively articulate the subjective views or requirements of the task. This may involve developing new property tests, proof strategies, or trust strategies tailored to the specific task.
For example, for tasks like sentiment analysis or text summarization, the property strategy could involve checking for specific sentiment patterns or summarization structures. The proof strategy could involve constructing evidence-based summaries or sentiment analyses to validate the LLM's output. The trust strategy could focus on verifying the consistency of sentiment predictions or summary generation across multiple runs or datasets.
Additionally, the extension of the oracle-checker scheme to more complex tasks may require the incorporation of domain-specific knowledge or constraints into the checking process. This could involve integrating external resources or expert knowledge to enhance the checker's ability to evaluate the trustworthiness of the LLM's outputs for those tasks.

What are the potential limitations or drawbacks of the oracle-checker approach compared to other trustworthiness evaluation methods for generative LLMs?

While the oracle-checker approach offers a structured and systematic way to evaluate the trustworthiness of generative LLMs, it also has some limitations compared to other evaluation methods.
One potential limitation is the reliance on the oracle (LLM) for providing accurate outputs. If the LLM itself is prone to errors or biases, the checker's evaluations may be influenced by these inaccuracies, leading to potential false positives or false negatives in the trustworthiness assessment.
Another drawback is the computational complexity of running multiple tests or queries to validate the LLM's outputs. This can be resource-intensive and time-consuming, especially for large-scale evaluations or complex tasks, making the oracle-checker approach less scalable compared to automated evaluation methods.
Furthermore, the subjective nature of defining the checking strategies and designing the checkers can introduce bias or inconsistency in the evaluation process. Human judgment and interpretation play a significant role in determining the effectiveness of the oracle-checker approach, which can lead to variability in the trustworthiness assessments.

How can the design of the checkers be further optimized to balance the trade-offs between the different checking strategies (property, proof, trust)?

To optimize the design of the checkers and balance the trade-offs between the different checking strategies (property, proof, trust), several approaches can be considered:

Adaptive Checker Design: Develop adaptive checkers that can dynamically adjust the emphasis on each checking strategy based on the task requirements or the characteristics of the LLM's outputs. This flexibility can help optimize the checker's performance for different scenarios.

Ensemble Approach: Implement an ensemble of checkers that combine multiple strategies to provide a more comprehensive evaluation. By leveraging the strengths of each strategy, the ensemble approach can enhance the overall trustworthiness assessment and mitigate the limitations of individual strategies.

Feedback Mechanism: Introduce a feedback mechanism that allows the checker to learn from its evaluations and improve over time. By incorporating feedback from previous assessments, the checker can adapt its strategies and decision-making processes to achieve better balance and accuracy in evaluating the LLM's outputs.

Threshold Adjustment: Fine-tune the thresholds or criteria used in each checking strategy to optimize the trade-offs between precision and recall. By adjusting the acceptance criteria based on the task requirements or the desired level of trustworthiness, the checker can achieve a better balance in evaluating the LLM's outputs.

By implementing these optimization strategies, the design of the checkers can be enhanced to effectively balance the trade-offs between the different checking strategies and improve the overall trustworthiness evaluation of generative LLMs.