A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024)

Abstract: Reasoning about subjective natural language descriptions, such as opinions and preferences, is a challenging topic that largely remains unsolved to date. In particular, state-of-the-art large language models (LLMs) perform disappointingly in this task, show strong biases, and do not meet the interpretability requirements often needed in these kinds of applications. We propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. We apply supervised graph learning, offer explanations for the model’s reasoning, and show that our model performs well across all 15 topics of OpinionQA, outperforming several prominent LLMs. Our detailed analysis further shows its unique advantages and the complementary nature it offers in comparison to LLMs.

## introduction subjective knowledge such as personal opinions and preferences represents a considerable challenge for automated reasoning. in fact, on the recently proposed opinionqa benchmark @xcite , a collection of 15 subsets of survey questions by the pew research center foot_1 , even the stateof-the-art large language models (llms) reach surprisingly low scores and reveal certain biases @xcite . as llms are incorporated into applications aimed at assisting individuals in daily tasks and decision-making @xcite @xcite , it is imperative that they can personalize their outputs for individual users. one of the inherent problems with reasoning with subjective knowledge is its implicit nature. rather than explicitly specifying their preferences and opinions, users may express these opinions indirectly through continuous interactions. other properties that affect opinions and preferences may be external to the discourse, such as demographic information and cultural background @xcite . finally, we observe that various aspects of a problem are usually related, and the models often have to combine various pieces of information. to test llms' ability to learn personal opinions, the opinionqa datasets present models with the dialogue history containing a participant's responses to survey questions (e.g., do you use a password manager to help keep track of your online passwords?), as well as their demographic information (e.g., age: 50-64, political affiliation: republican). the model is then tasked with answering a set of multiple-choice questions pertaining to the opinions (e.g., yes/no). current state-of-the-art llms still perform poorly on opinionqa @xcite . in particular, models often ignore the survey history and over-rely on demographic information, which may lead to perpetuating societal biases @xcite . moreover, english llms struggle with questions from cross-national surveys @xcite , given that they are trained on english web text coming primarily from users in the us. current solutions focus on improving the reasoning by filtering the information that is available to the model when making a certain judgment @xcite , but there is still considerable room for improvement. further, observe that general-purpose llms may not best suit this task by design, specifically alignment, since sycophancy (i.e., models adjusting their responses to align with a human user's perspective, even when that perspective lacks objective correctness) is considered an undesirable behavior @xcite . we propose an alternative approach to reasoning about subjective descriptions, inspired by traditional techniques modeling the relational nature of complex conceptual knowledge in semantic networks @xcite . our framework, depicted in figure 1 , creates one opinion graph per individual, explicitly modeling relationships between their opinions on various topics (green). due to the often intricate and implicit nature of opinions, we complete the graph with derived knowledge generated by an llm (yellow). finally, we add auxiliary nodes for the answer choices (blue, rose) and apply supervised graph learning to determine the opinions that are most relevant to the given question. our approach outperforms prominent llms across most of the 15 opinionqa subsets. we ablate and evaluate our approach in detail. most importantly, our analysis shows that our answers often complement those of the llms, which offers interesting future research potential. finally, the graph neural network allows for extracting the attention flow over the graph nodes and hence naturally delivers an explanation for its reasoning. while the explanations are not perfect, they are useful for analyzing the reasoning steps and hint at future research questions. 3 2 related work reasoning about subjective descriptions. simpler forms of reasoning over subjective text have been studied in nlp for a long time in tasks such as sentiment prediction or user-item recommendation @xcite @xcite . more complex tasks, predicting an opinion based 3 we will make the code available upon publication. on other opinions, have been considered recently with the study of personalized question answering over surveys @xcite . overall, llms have been shown to be underperforming @xcite . among their many findings, we point out the importance of curated personal opinions for personalized prediction @xcite . understanding the model's ability to reason about human opinions is crucial to ensure safer alignment with a user's ethical principles, moral beliefs, and culture-specific values. we build upon the previous works by focusing on opinion data and employing graph learning to select opinions relevant to the task at hand. ## conclusions we propose a novel approach for reasoning about subjective natural language descriptions. our approach represents a person's opinions in a graph which also includes generated implications, explicitly modeling the relationships between various statements. given a question about a previously unstated opinion, we apply supervised graph learning to find reasoning paths from the existing knowledge to one of the candidate answers. our model outperforms several prominent language models across all 15 topics of opinionqa, while also offering explanations for its predictions. detailed analysis further shows our model's unique advantages and the complementary nature it offers, in comparison to llms. altogether, our work proposes a promising research direction to address this challenging problem and opens up interesting future research.