Sequence-Level Certainty Reduces Hallucination in Knowledge-Grounded Dialogue Generation

Kernekoncepter

In this work, the authors propose sequence-level certainty as a key factor in reducing hallucination in Knowledge Grounded Dialogue Generation (KGDG) models. They introduce Certainty-based Response Ranking (CRR) methods to mitigate model hallucination during decoding time.

Resumé

The content explores the correlation between sequence-level certainty and hallucination in KGDG models. It introduces two types of CRR approaches, Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR), to reduce model hallucination effectively. Experimental results across various datasets, decoding methods, and models validate the effectiveness of these methods.

Key points:

• Proposal of sequence-level certainty as crucial for reducing hallucination in KGDG.
• Introduction of Certainty-based Response Ranking (CRR) methods.
• Validation through experiments on different datasets, decoding methods, and models.

Statistik

Sequence-level probabilistic certainty can be calculated as: 1/N * Σ(log p(si|s<i)), where p(si|s<i) is the conditional probability of generating token si given past tokens.
Agreement Score (AS) is used to measure semantic certainty by calculating the summed probability of semantic entailment between a response candidate and all others.

Citater

"Higher levels of both types of certainty are correlated with lower levels of hallucination in model outputs."
"CRR significantly reduces hallucinations across all experiment settings."

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Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation

by Yixin Wan,Fa... kl. arxiv.org 03-06-2024

https://arxiv.org/pdf/2310.18794.pdf

Dybere Forespørgsler

How does the proposed CRR method compare to other existing mitigation strategies for model hallucination

The proposed Certainty-based Response Ranking (CRR) method offers a unique approach to mitigating model hallucination compared to existing strategies. While previous methods have focused on token-level uncertainty or predictive uncertainty, CRR introduces sequence-level certainty as a common theme over hallucinations in Knowledge Grounded Dialogue Generation (KGDG). By dissecting certainty into probabilistic and semantic categories, CRR ranks response candidates based on their overall certainty levels rather than focusing on individual tokens. This holistic approach allows for a more comprehensive evaluation of the generated responses, leading to improved mitigation of model hallucination.

What implications do these findings have for improving the overall performance of KGDG models

The findings from this study hold significant implications for enhancing the overall performance of KGDG models. By establishing a correlation between sequence-level certainty and hallucination, researchers can now leverage this insight to develop more effective mitigation strategies. The introduction of CRR as a decoding-time method provides a practical solution for reducing model hallucination by prioritizing responses with higher certainty levels. Implementing CRR in KGDG models can lead to more accurate and faithful dialogue generation, ultimately improving the quality and reliability of the generated responses.

How might incorporating external knowledge sources impact the effectiveness of sequence-level certainty in reducing model hallucinations

Incorporating external knowledge sources can greatly enhance the effectiveness of sequence-level certainty in reducing model hallucinations within KGDG models. External knowledge serves as a valuable reference point for evaluating the faithfulness and accuracy of generated responses. By aligning sequence-level certainty with semantic contents derived from external knowledge sources, models can better assess the relevance and coherence of their outputs. This integration enables KGDG models to generate responses that are not only consistent with provided knowledge but also exhibit higher levels of semantic understanding and fidelity.