RATFtouille: Evaluating Emotional and Goal-Directed Reasoning Capabilities of Language Models Using Cooking Recipes

While much of the current study of LLM reasoning is focused on goal-directed reasoning and emotional reasoning as two separate tasks, there are many real-world scenarios where LLMs are required to do them jointly. For example, if a user deletes a photo and then expresses strong sadness, an assistant should infer that the deletion was likely unintended and that the user wants the photo recovered. In cases like these, an LLM must infer a user's emotional state and use that inference to perform reasoning on their goals. In order to systematically evaluate LLMs abilities to perform emotionally contextualized goal-directed reasoning (ECGD), we propose the Recipe-Actor Transcript Framework (RATF), a framework that procedurally generates step-by-step transcripts of simulated actors following recipes to make food, where the recipes are sourced from the RecipeNLG dataset. We utilize these transcripts to test whether an LLM can correctly identify the intents and goals of a simulated actor, and predict their next action. More precisely, we apply RATF to study two LLM abilities: (1) goal prediction, where LLMs must infer the actor's goal given their past behavior, i.e., what are they trying to cook, and (2) next-action prediction, where we evaluate the accuracy of the LLM's predicted next-action distribution of the simulated actor when given in-context examples of actor behavior. We test several frontier LLMs and find that LLMs struggle with these tasks, and their performance is highly sensitive to surface-level variations in the way the prompt is presented. We find evidence suggesting that LLMs rely on heuristics to solve this task rather than performing true ECGD reasoning.