Incorporating Abstract Meaning Representation into Large Language Models for Robust Open-Domain Dialogue Evaluation

Concetti Chiave

Combining domain-specific language models with AMR graph information and large language models can improve the robustness of open-domain dialogue evaluation, especially for discriminating adversarial negative responses.

Sintesi

The paper proposes a framework that combines domain-specific language models (SLMs) with large language models (LLMs) for open-domain dialogue evaluation. The key aspects are:

1. The SLM leverages both sentence-level and AMR graph-level information to learn enhanced semantic representations. A gating mechanism is used to fuse the two types of representations, and a contrastive loss is introduced to align the sentence and graph features.

2. The output score from the SLM and the AMR graph information are then integrated into the prompt of the LLM to provide domain-specific knowledge and improve the in-context learning performance.

3. Experiments on the DailyDialog++ and Personachat datasets show that the proposed method outperforms a wide range of baselines, including LLM-based methods, especially in discriminating adversarial negative responses.

The framework effectively incorporates structured semantic information from AMR graphs into the dialogue evaluation process, making it more robust to challenging adversarial examples compared to existing approaches.

Statistiche

The DailyDialog++ dataset contains 9,259 training, 1,028 validation, and 1,142 test context-response pairs, with 5 positive, 5 random negative, and 5 adversarial negative responses per context.
The Personachat dataset was used to create adversarial negative examples by randomly selecting an utterance from the context and inserting it as a response.

Citazioni

"Automatic open-domain dialogue evaluation has attracted increasing attention."
"Trainable evaluation metrics are commonly trained with true positive and randomly selected negative responses, resulting in a tendency for them to assign a higher score to the responses that share higher content similarity with a given context."
"AMR graphs can capture the internal state of a dialogue system and offer complementary semantic knowledge."

Approfondimenti chiave tratti da

Structured Information Matters

by Bohao Yang,K... alle arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01129.pdf

Domande più approfondite

How can the proposed framework be extended to handle multi-turn dialogues and incorporate additional contextual information beyond the current turn?

To extend the proposed framework for multi-turn dialogues, we can incorporate memory mechanisms to store information from previous turns. This would allow the model to maintain context across multiple exchanges and make more informed evaluations. Additionally, we can implement attention mechanisms to focus on relevant parts of the conversation history and integrate them with the current turn for a comprehensive understanding.
Furthermore, to incorporate additional contextual information beyond the current turn, we can explore the use of knowledge graphs or ontologies. By leveraging external knowledge sources, the model can access a wider range of information to enhance its understanding of the dialogue context. This could involve integrating domain-specific knowledge bases or incorporating real-time external data sources to enrich the dialogue evaluation process.

How can the insights from this work be applied to enhance the performance of open-domain dialogue systems, beyond just the evaluation task?

The insights from this work can be applied to enhance the performance of open-domain dialogue systems in various ways. Firstly, the integration of structured knowledge representations like AMR graphs can improve the semantic understanding of dialogue responses, leading to more contextually relevant and coherent interactions. This can enhance the overall quality of generated responses in open-domain dialogue systems.
Moreover, the framework's focus on handling adversarial negative examples can help in training dialogue systems to generate more diverse and appropriate responses. By incorporating mechanisms to discriminate between different types of responses, the system can learn to avoid generating misleading or irrelevant answers.
Additionally, the use of domain-specific language models and the incorporation of external knowledge sources can enhance the system's ability to generate informative and accurate responses tailored to specific domains or topics. This can result in more engaging and meaningful interactions with users in open-domain dialogue systems.