Neurosymbolic approaches can add robustness to opaque neural systems by incorporating explainable symbolic representations. However, previous approaches have not used formal logic to contextualize queries to and validate outputs of large language models (LLMs). We propose ProSLM, a novel neurosymbolic framework, to improve the robustness and reliability of LLMs in question-answering tasks. We provide ProSLM with a domain-specific knowledge base, a logical reasoning system, and an integration to an existing LLM. This framework has two capabilities (1) context gathering: generating explainable and relevant context for a given query, and (2) validation: confirming and validating the factual accuracy of a statement in accordance with a knowledge base (KB). Our work opens a new area of neurosymbolic generative AI text validation and user personalization.
Despite Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP), their capability of performing logical reasoning and automated planning is still debated. In this context, the state of the art is PlanGPT, a GPT-2 model specifically trained for planning tasks. This recent approach provides GPT-based planning policies with remarkable performance, but it can generate invalid plans containing violated action preconditions or unsatisfied goals. To address this limitation, we propose an extension of PlanGPT that integrates a plan validator into the generation process. The validator is exploited to prune invalid plan prefixes during the GPT token generation, obtaining a more robust and powerful solution to planning via GPT. We empirically evaluate the effectiveness of our approach and demonstrate its potential in various planning domains.
Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in drug discovery and materials science. While recent work has primarily focused on data-driven approaches, the KANO model introduces a novel paradigm by incorporating knowledge-enhanced pre-training. In this work, we expand upon KANO by integrating the large-scale ChEBI knowledge graph, which includes 2,840 functional groups – significantly more than the original 82 used in KANO. We explore two approaches, Replace and Integrate, to incorporate this extensive knowledge into the KANO framework. Our results demonstrate that including ChEBI leads to improved performance on 9 out of 14 molecular property prediction datasets. This highlights the importance of utilizing a larger and more diverse set of functional groups to enhance molecular representations for property predictions.
Neuro-symbolic approaches have garnered much interest recently as a path toward endowing neural systems with robust reasoning capabilities. Most proposed end-to-end methods assume knowledge to be given in advance and do not scale up over many latent concepts. The recently proposed Embed2Sym tackles the scalability limitation by performing end-to-end neural training of a visual perception component from downstream labels to generate clusters in the latent space of symbolic concepts. These are later used to perform downstream symbolic reasoning but symbolic knowledge is still engineered. Taking inspiration from Embed2Sym, this paper introduces a novel method for scalable neuro-symbolic learning of first-order logic programs from raw data. The learned clusters are optimally labelled using sampled predictions of a pre-trained vision-language model. A SOTA symbolic learner, robust to noise, uses these labels to learn an answer set program that solves the reasoning task. Our approach, called Embed2Rule, is shown to achieve better accuracy than SOTA neuro-symbolic systems on existing benchmark tasks in most cases while scaling up to tasks that require far more complex reasoning and a large number of latent concepts.
While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in Computer Vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic errors on important subsets of data called slices, which are groups of data sharing a set of attributes. The slice discovery problem involves detecting semantically meaningful slices on which the model performs poorly, called rare slices. We propose a modular Neurosymbolic AI approach whose distinct advantage is the extraction of human-readable logical rules that describe rare slices, and thus enhances explainability of CV models. To this end, we present a methodology to induce rare slice occurrences in a model. Experiments on datasets from our data generator leveraging on Super-CLEVR show that the approach can correctly identify rare slices and produce logical rules describing them. The rules can be fruitfully used to generate new training data to mend model behavior or may be integrated into the model to enhance its inference capabilities. (The code for reproducing our experiments is available as an online repository: https://gitlab.tuwien.ac.at/kbs/nesy- ai/ilp4sd).
