Evaluating Dialogue Systems: The Impact of User Feedback on Crowdworkers and Large Language Models

Conceptos Básicos

User feedback from follow-up utterances significantly influences the evaluation of dialogue systems by both crowdworkers and large language models.

Resumen

This study investigates the effect of user feedback from follow-up utterances on the evaluation of task-oriented dialogue systems (TDSs) by both crowdworkers and large language models (LLMs). The researchers conducted experiments with two setups: one that provided only the initial user query and the system's response, and another that included the user's follow-up utterance.

The key findings are:

1. Both crowdworkers and LLMs exhibit sensitivity to user feedback from follow-up utterances, with significant differences in their ratings across the two setups, except for relevance.

2. Crowdworkers are more susceptible to user feedback on usefulness and interestingness compared to LLMs, who are more influenced by user feedback on interestingness and relevance.

3. User feedback leads to a more personalized assessment of usefulness by crowdworkers, aligning closely with the user's explicit feedback.

4. In cases of ambiguous or complex user requests, user feedback improves agreement among crowdworkers, helping to clarify the user's intent.

These findings highlight the importance of incorporating user feedback in the evaluation of dialogue systems and suggest the potential for automated feedback integration in future research.

Estadísticas

"I'm looking for a light but thought-provoking movie, similar to "Inception" or "The Grand Budapest Hotel". Any suggestions?"
"Hello I can recommend you "The Matrix". It's a delightful and heartwarming film."
"That's not what I had in mind, I want Christopher Nolan movies?"

Citas

"User feedback from follow-up utterances significantly influences the evaluation of dialogue systems by both crowdworkers and large language models."
"Crowdworkers are more susceptible to user feedback on usefulness and interestingness compared to LLMs, who are more influenced by user feedback on interestingness and relevance."
"In cases of ambiguous or complex user requests, user feedback improves agreement among crowdworkers, helping to clarify the user's intent."

Ideas clave extraídas de

Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs

by Clemencia Si... a las arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12994.pdf

Consultas más profundas

How can the insights from this study be leveraged to improve the automatic evaluation of dialogue systems?

The insights from this study can be instrumental in enhancing the automatic evaluation of dialogue systems by incorporating user feedback mechanisms. By understanding the significant impact of user feedback, particularly follow-up utterances, on the evaluation of system responses, researchers can develop more sophisticated evaluation metrics that consider the context of the conversation. This could involve designing algorithms that analyze user feedback to provide more personalized and accurate assessments of system performance. Additionally, leveraging machine learning models, such as large language models (LLMs), to mimic human annotator behavior in considering user feedback could lead to more reliable and consistent automatic evaluations of dialogue systems.

What other contextual factors, beyond user feedback, could influence the evaluation of dialogue systems by human and machine annotators?

Apart from user feedback, several other contextual factors could influence the evaluation of dialogue systems by both human and machine annotators. These factors include the tone and sentiment of the conversation, the level of engagement and responsiveness of the user, the complexity of the user requests, the domain knowledge required to understand the dialogue, and the cultural background of the users. Additionally, the presence of visual cues or multimedia elements in the conversation, the timing and sequence of the dialogue, and the overall user experience during the interaction could also impact the evaluation process. Considering these contextual factors alongside user feedback can provide a more comprehensive assessment of dialogue system performance.

How might the findings from this study on the impact of user feedback apply to other interactive systems beyond dialogue, such as search or recommendation engines?

The findings from this study on the impact of user feedback can be extrapolated to other interactive systems beyond dialogue, such as search or recommendation engines, to enhance their evaluation processes. By incorporating user feedback mechanisms similar to follow-up utterances, these systems can better understand user preferences, needs, and satisfaction levels. This can lead to more personalized and relevant search results or recommendations, improving the overall user experience. Additionally, leveraging insights from user feedback can help in refining the algorithms used in these systems, making them more efficient and effective in delivering tailored outcomes to users. Overall, the principles of user feedback highlighted in this study can be applied across various interactive systems to optimize their performance and user satisfaction.