13 accepted papers
We introduce ACPBench Hard, a dataset of generative, open-ended questions which LLM models needs to answer in order to plan. Models that perform well on these tasks could in principle be integrated into a planner or be used directly as a policy. We discuss the complexity of these tasks as well as th…
We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query language dialects or obscure their reasoning; QueryGym instead requires agents to construct explicit sequences of relational
There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multistep reasoning. As a result, it is imperative to evaluate LLMs on core skills required for planning. In this work, we present AC…
Large language models (LLMs) now turn their attention to search. Recently, Thought of Search (ToS) proposed defining the search space with code, having an LLM produce that code. ToS requires a human in the loop, collaboratively producing a sound successor function and goal test, achieving impressive…
Recent work has considered whether large language models (LLMs) can function as planners: given a task, generate a plan. We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain.…
The creation of planning models, and in particular domain models, is among the last bastions of tasks that require exten- sive manual labor in AI planning; it is desirable to simplify this process for the sake of making planning more accessi- ble. To this end, we investigate whether large language m…
Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for the vast collection of recently proposed methods for planning with large language models. In this work, we alleviate this…
Planning tasks succinctly represent labeled transition systems, with each ground action corresponding to a label. This granularity, however, is not necessary for solving planning tasks and can be harmful, especially for model-free methods. In order to apply such methods, the label sets are often man…
Code style refers to attributes of computer programs that affect their readability, maintainability, and performance. Enterprises consider code style as important and enforce style requirements during code commits. Tools that assist in coding style compliance and transformations are highly valuable.…
Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering…
Code understanding is an increasingly important application of Artificial Intelligence. A fundamental aspect of understanding code is understanding text about code, e.g., documentation and forum discussions. Pre-trained language models (e.g., BERT) are a popular approach for various NLP tasks, and…
We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in th…
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to obviate the need for such heuristics, however, its deployment in automated theorem proving remains a challenge. In this p…