Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models (2024)

Abstract: Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. Our proposed method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown.

## introduction general-purpose conversational systems such as chatgpt and copilot are revolutionizing how people live and work. understanding when and why users are satisfied or dissatisfied is critical for the continuous improvement of these systems. it helps system developers identify areas of improvements, conduct effective a/b experiments, and optimize underlying models. unsurprisingly, developing machine learning models for user satisfaction estimation (use) @xcite @xcite @xcite has captured significant attention from the research community. when estimating user satisfaction, simply classifying that a user is satisfied or dissatisfied is insufficient. understanding the reason why a user is satisfied or dissatisfied is just as valuable. for example, frequent query reformulation presents opportunities for prompt recommendation and conversations where users explicitly correct a bot's mistakes can suggest examples for model alignment. see figure 1 for an illustration. however, most existing work has focused on improving classification accuracy and has overlooked interpretability. representation learning-based approaches @xcite @xcite are relatively opaque due to their use of neural models (e.g., embeddings) and thus offer little insight into conversational patterns that indicate satisfaction/dissatisfaction. similar limitations apply to reward models for training llms, e.g., rlhf @xcite and rlaif @xcite . in this case, the learned model produces a continuous "reward" score that aims to distinguish outputs that a human prefers without explaining why a conversation has a higher score than others. to our knowledge, these reward models have not been directly used for use, but we treat it as a baseline due to their ability to rank outputs with respect to human preferences. some prior work addressed the interpretation needs of use via featurized ml models. examples include @xcite , which evaluated user satisfaction based on human-annotated features assessing task success and dialogue costs, and @xcite , which proposed domainindependent features that evaluate response quality. however, the growth of llm-based conversational systems (e.g., chatgpt, bing copilot) means user queries in conversational systems may now be across multiple domains and intents (e.g., taskoriented, qa, chitchat). as such, approaches based on domain-specific features have limited generalizability to these diverse conversational patterns @xcite . in this work, we make the key observation that llms can achieve both high classification accuracy and fine-grained interpretability at the same time -through their ability to reason about user conversational patterns and identify salient pattern classes that generalize and produce accurate predictions. we propose supervised prompting for user satisfaction rubrics (spur). we consider a fewshot scenario, where a small number of training examples are available, and develop a supervised, iterative prompting framework that uses an llm to (1) extract signals of satisfaction from user utterances in a labeled training set, (2) summarize the reasons into rubrics for identifying satisfaction/dissatisfaction conversational patterns, and (3) apply the rubrics to predict satisfaction labels on unseen conversations. in addition to being more accurate, our approach provides an interpretable rubric for understanding the conversational patterns that indicate user satisfaction/dissatisfaction. notably, our approach can be used to learn sat/dsat patterns automatically for different conversational systems. in our experimental results, we show the distributions of patterns in different types of systems and demonstrate how these patterns (1) correlate to overall user satisfaction, and (2) differ across domains. moreover, we show that we can scale the application of the learned rubrics in two ways. first, we show that we can distill individual rubric items into an embedding-based model that can be applied at scale without the need for llm prompting. next, we show that we can add rubric items as features to an embedding-based model to increase the accuracy of embedding-only models on datasets with more available training data. the main contributions of our work include: here u t refers to a user utterance and a t refers to an ai agent utterance. the user-agent utterances c i typically consist of multiple turns, e.g., @xmath0. the conversation also has an overall user satisfaction label y @xmath1, +1] provided by thumb feedback (e.g., like or dislike). our goal is to learn a function f : @xmath2. in multi-turn conversational sessions, a user can convey their satisfaction (or dissatisfaction) explicitly in their utterances or implicitly through their behavioral interactions with the agent. we refer to these satisfaction/dissatisfaction conversational patterns as sat/dsat patterns. let @xmath3, s 2 , • • • , s ∞ } and @xmath4, d 2 , • • • , d ∞ } be the set of all interpretable sat and dsat patterns respectively. we assume these are latent and unknown. the goal is to identify a subset of sat/dsat pattern classes (s s ⊂ s, d s ⊂ d) that summarize the conversation enough to accurately predict its label: sat and dsat patterns may be direct compliments or complaints about the ai agent's responses, or behavioral patterns that implicitly express user satisfaction. for example, users may continue to ask follow-up questions, indicating that the ai has provided accurate information that inspires their curiosity and leaves them satisfied. conversely, if a user repeatedly rephrases the same question, it can signal dissatisfaction. ## spur we propose spur for interpretable user satisfaction estimation (use) given a small set of labeled conversation c from a conversational system. due to the multi-turn and general-purpose nature in such conversational systems, users demonstrate a variety of response patterns when expressing satisfaction or dissatisfaction. our approach follows the three-phase prompting strategy depicted in figure 2 : supervised extraction, rubric summarization, and user satisfaction estimation. our three-phase approach is essential for ensuring accuracy, generalization, and interpretability. through supervised extraction, spur improves accuracy by capturing the diverse conversational patterns in the training set c @xmath5, c 2 , • • • , c n }, which are annotated with thumb feedback. in the rubric summarization stage, the llm improves generalization and interpretability by identifying prominent sat and dsat pattern classes among the full set of extracted pattern. finally, spur uses the learned rubrics generated from the previous stage to score user satisfaction on unlabeled conversations. for the ease of understanding, we use mathematical definitions to approximate the process of spur in the following three sections. ## evaluation we evaluate spur by comparing its performance quantitatively against previous embedding-based approaches and several ablated versions of our llm-based approach. ## conclusion and limitations in this paper, we proposed supervised prompting for user satisfaction rubrics (spur), a novel framework for estimating user satisfaction with llms in conversational systems. we demonstrated that spur outperforms existing methods on user satisfaction estimation across different types of conversational systems and also provided insights into the factors that influence user satisfaction. moreover, spur is more interpretable because it automatically grounds/scores the dimensions of satisfaction in observed user behavior from rubric summarization prompting. we also demonstrated the utility of our rubrics for knowledge distillation and coverage analysis. finally, we showed the utility of our model for different training set sizes by combining the rubric item scores with the conversational embeddings as features and observed that these rubrics provide extra signals for performance improvement on use. limitations. although spur outperforms baseline models with limited training sets, an important factor, the framework is costly if the goal is to estimate user satisfaction at the scale of millions of conversations. we have proposed a method to distill knowledge from gpt-4, but a thorough study is needed to show the robustness of this approach. in future work, we will focus on the scalability issues of spur to reduce its cost at a larger scale.