HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection (2409.17504v1)

Abstract: The surge in applications of LLMs has prompted concerns about the generation of misleading or fabricated information, known as hallucinations. Therefore, detecting hallucinations has become critical to maintaining trust in LLM-generated content. A primary challenge in learning a truthfulness classifier is the lack of a large amount of labeled truthful and hallucinated data. To address the challenge, we introduce HaloScope, a novel learning framework that leverages the unlabeled LLM generations in the wild for hallucination detection. Such unlabeled data arises freely upon deploying LLMs in the open world, and consists of both truthful and hallucinated information. To harness the unlabeled data, we present an automated membership estimation score for distinguishing between truthful and untruthful generations within unlabeled mixture data, thereby enabling the training of a binary truthfulness classifier on top. Importantly, our framework does not require extra data collection and human annotations, offering strong flexibility and practicality for real-world applications. Extensive experiments show that HaloScope can achieve superior hallucination detection performance, outperforming the competitive rivals by a significant margin. Code is available at https://github.com/deeplearningwisc/haloscope.

HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection

The paper "HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection" addresses a pressing challenge in the deployment of LLMs—the detection of hallucination, i.e., the generation of information that is plausible but untruthful. This work presents HaloScope, a novel learning framework designed to detect hallucinations by utilizing unlabeled LLM outputs, which are generated in vast quantities when LLMs are deployed in real-world applications.

Core Contributions

1. Leveraging Unlabeled Data: One of the primary innovations of HaloScope is its ability to harness unlabeled LLM generations, which removes the dependency on labor-intensive and error-prone human annotations. These unlabeled generations are characterized by a mix of truthful and hallucinated information, analogous to datasets encountered by LLMs in real-world scenarios.
2. Automated Membership Estimation Scoring: HaloScope introduces an automated membership estimation score that leverages the latent representations of LLMs to identify whether a generation is truthful or hallucinated. This score is computed by identifying a subspace in the activation space that is associated with hallucinated statements. The key insight is that hallucinated data tends to have representations that align strongly with certain directions in this subspace.
3. Binary Truthfulness Classifier: Based on the membership estimation score, HaloScope subsequently trains a binary classifier to categorize unlabeled data into truthful or hallucinated classes. This classifier does not require additional data collection or human annotations, enhancing its practical applicability.

Methodology

The framework comprises two primary steps:

1. Membership Estimation via Latent Subspace:
   • Embeddings are extracted from the LLM on the provided unlabeled data. Singular Value Decomposition (SVD) is performed on these embeddings to identify the dominant directions in the latent space.
   • The membership estimation score measures the norm of the projection of an embedding onto the identified directions (top singular vectors), distinguishing between truthful and hallucinated data.
2. Truthfulness Classification:
   • Data with high membership scores are tentatively classified as hallucinated, while lower scores suggest truthfulness.
   • A binary classifier is trained to refine these initial categorizations, utilizing representations to improve classification accuracy.

Experimental Validation

Extensive experiments demonstrate that HaloScope outperforms competitive baselines in hallucination detection across diverse datasets, including open-book and closed-book question-answering tasks. The results reveal substantial improvements in AUROC, particularly highlighted by a 10.69% increase on the challenging TruthfulQA benchmark, reaching an AUROC of 78.64% compared to the supervised upper bound of 81.04%.

Further ablation studies show the robustness of the proposed scoring function and emphasize the importance of leveraging intermediate layers of LLM representations for effective hallucination detection.

Practical and Theoretical Implications

Practically, HaloScope provides a flexible and scalable solution for hallucination detection without the need for curated labeled data. This flexibility is critical for real-world applications where dynamic data environments necessitate adaptive solutions. Moreover, the proposed framework is computationally efficient, benefiting from intrinsic properties of LLM representations to identify hallucinations.

Theoretically, the paper contributes to a deeper understanding of LLM latent spaces and their correlation with factual accuracies of generated outputs. The methodology underscores the potential of utilizing unsupervised or minimally supervised approaches to improve AI safety and reliability.

Future Directions

Future research is suggested to explore distributionally robust algorithms for cases of significant distribution shifts between training and test data. Additionally, extending HaloScope to other generative tasks like text summarization and dialogue systems holds promise for broader applications. Investigating adaptive scoring mechanisms that dynamically adjust to evolving model behaviors could further enhance robustness.

Conclusion

In conclusion, the paper presents HaloScope as a compelling approach for hallucination detection by leveraging unlabeled LLM generations. The empirical evidence and methodological innovations underline HaloScope's potential to enhance the reliability of LLMs in real-world scenarios, thereby advancing the broader goal of safe and trustworthy AI deployments.