Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering (2025)

Abstract: Author profiling is the task of inferring characteristics about individuals by analyzing content they share. Supervised machine learning still dominates automatic systems that perform this task, despite the popularity of prompting large language models to address natural language understanding tasks. One reason is that the classification instances consist of large amounts of posts, potentially a whole user profile, which may exceed the input length of Transformers. Even if a model can use a large context window, the entirety of posts makes the application of API-accessed black box systems costly and slow, next to issues which come with such “needle-in-the-haystack” tasks. To mitigate this limitation, we propose a new method for author profiling which aims at distinguishing relevant from irrelevant content first, followed by the actual user profiling only with relevant data. To circumvent the need for relevance-annotated data, we optimize this relevance filter via reinforcement learning with a reward function that utilizes the zero-shot capabilities of large language models. We evaluate our method for Big Five personality trait prediction on two Twitter corpora. On publicly available real-world data with a skewed label distribution, our method shows similar efficacy to using all posts in a user profile, but with a substantially shorter context. An evaluation on a version of these data balanced with artificial posts shows that the filtering to relevant posts leads to a significantly improved accuracy of the predictions.

## introduction author profiling aims at inferring information about individuals by analyzing content they share. a large and diverse set of characteristics like age and gender @xcite @xcite , native language @xcite , educational background @xcite , personality @xcite @xcite , or ideology @xcite have been studied so far. author profiling is often formulated supervised learning in which a full user profile with possibly hundreds or thousands of individual textual instances constitutes the input. despite the success of deep learning strategies in various natural language processing tasks, such approaches often underperform when applied to author profiling @xcite . one factor contributing to this may be that models like bert @xcite have constraints on the length of the input they can process, preventing them from processing all content linked to an author at once. another reason for this may be that not all content shared by an author is equally useful when predicting certain characteristics. some of the content may even be considered noise, making it difficult for machine learning models to grasp patterns needed when predicting specific characteristics of an author -we are faced with a "needle-inthe-haystack" challenge foot_0 . with this paper, we approach this challenge and propose to prefilter posts to distinguish between helpful and misleading content before inferring a characteristic. thereby, accuracy of automated profiling systems could be enhanced, and computational requirements could be reduced. to induce such filter without data manually annotated for relevancy, we study reinforcement learning with a reward function that represents the expected performance gain of a prompt-based system. therefore, our approach only requires a prompt for a large language model (llm) and leads to a prefiltering classifier that can, at test time, be applied with a limited number of queries to a large language model. in contrast to retrieval augmented generation setups @xcite , our setup has the advantage that it does not need to rely on the ad-hoc abilities of a retrieval system. our contributions are therefore foot_1 : ## rl-profiler: reinforcement learning by llm-based performance rewards we assume a profile consisting of a set of textual instances as input, with annotations on the profile, but not instance level, during training. we optimize the instance-relevance filter with information from a profile-level prediction model. this filter decides which textual instances are informative and should be used for the profile-level decision. figure 1 illustrates this architecture. our rl-profiler is devided into (1) the selection network (selnet) and (2) the classification network (cnet). selnet corresponds to an agent in the rl sense and selects textual instances from a profile. cnet then uses these instances to predict a profilelevel label. during training (left side of figure 1 ), we compare this prediction with the given profilelevel ground truth to calculate a reward. ## conclusion and future work we outlined a novel approach for automatic personality prediction from social media data which enables prompt-based predictions to focus on the most relevant parts of an input. notably, we do not require labels of relevance, but induce the filter only from the prompt-performance on the profile level. while the results on real data shows no performance improvement overall, it does decrease the required context window of the language model. with an experiment on artificial data, we can show a substantial performance improvement. this shows that our method helps the language model to focus on relevant content, instead of leaving this task to the attention mechanisms in the transformer. the present results provide several directions for future work: one direction is to replace or adapt individual parts of the proposed system. this includes the evaluation of other policy optimization algorithms, exploring the usage of different large language models, or experiment with different policy parameterization techniques. further, we suggest to study if the requirement for labeled profiles could be relaxed by relying on confidence estimates of the zero-shot classification. another interesting question would be if the relevancy assessement of rl-profiler is similar to what humans find relevant. this requires a future annotation study of relevancy in personality profiling. finally, it also remains interesting to explore how our approach performs when applied to predicting other concepts like gender or age. open.