Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. While Large Language Models (LLMs) have shown promise in generating human-like sentences for tasks such as question answering, paragraph summarization, and translation, they rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence, so that the missing information can be further requested and the correct response computed. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs, at least in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.
We present a novel neurosymbolic system called NeSyFOLD that classifies images while providing a logic-based explanation of the classification. NeSyFOLD's training process is as follows: (i) We first pre-train a CNN on the input image dataset and extract activations of the last layer filters as binary values; (ii) Next, we use the FOLD-SE-M rule-based machine learning algorithm to generate a logic program that can classify an imagerepresented as a vector of binary activations corresponding to each filter-while producing a logical explanation. The rules generated by the FOLD-SE-M algorithm have filter numbers as predicates. We use a novel algorithm that we have devised for automatically mapping the CNN filters to semantic concepts in the images. This mapping is used to replace predicate names (filter numbers) in the rule-set with corresponding semantic concept labels. The resulting rule-set is highly interpretable, and can be intuitively understood by humans. We compare our NeSyFOLD system with the ERIC system that uses a decision-tree like algorithm to obtain the rules. Our system has the following advantages over ERIC: (i) NeSyFOLD generates smaller rule-sets without compromising on the accuracy and fidelity; (ii) NeSyFOLD generates the mapping of filter numbers to semantic labels automatically.
Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. They also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP). We show how LLMs can be used to effectively extract knowledge-represented as predicates-from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.
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