Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation.For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe.We present a new corpus of cooking recipes enriched with descriptions of intermediate steps that describe the input and output for each step. PizzaCommonsense serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit input-output descriptions to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to beeasily memorized. GPT-4 achieves only 26% human-evaluated preference for generations, leaving room for future improvements.

Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations. However, for any human-interpretable concept, how can we find its direction in the latent space? We present a technique called linear relational concepts (LRC) for finding concept directions corresponding to human-interpretable concepts by first modeling the relation between subject and object as a linear relational embedding (LRE). We find that inverting the LRE and using earlier object layers results in a powerful technique for finding concept directions that outperforms standard black-box probing classifiers. We evaluate LRCs on their performance as concept classifiers as well as their ability to causally change model output.

Translating procedural text, like recipes, into a graphical representation can be important for visualizing the text, and can offer a machine-readable formalism for use in software. There are proposals for translating recipes into a flow graph representation, where each node represents an ingredient, action, location, or equipment, and each arc between the nodes denotes the steps of the recipe. However, these proposals have had performance problems with both named entity recognition and relationship extraction. To address these problems, we propose a novel framework comprising two modules to construct a flow graph from the input recipe. The first module identifies the named entities in the input recipe text using BERT, Bi-LSTM and CRF, and the second module uses BERT to predict the relationships between the entities. We evaluate our framework on the English recipe flow graph corpus. Our framework can predict the edge label and achieve the overall F1 score of 92.2, while the baseline F1 score is 43.3 without the edge label predicted.