1 code implementation • 3 Oct 2022 • Abulhair Saparov, He He
Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps).
2 code implementations • 6 May 2021 • Abulhair Saparov, Tom M. Mitchell
We derive and implement an inference algorithm that reads sentences by parsing and abducing updates to its latent world model that capture the semantics of those sentences, and evaluate it on two out-of-domain question-answering datasets: (1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be more representative of real language but still simple enough to focus on evaluating reasoning ability, while being robust against heuristics.
3 code implementations • ICLR 2020 • Emmanouil Antonios Platanios, Abulhair Saparov, Tom Mitchell
Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related tasks in more complex environments.
2 code implementations • CONLL 2017 • Abulhair Saparov
The work relies on a novel application of hierarchical Dirichlet processes (HDPs) for structured prediction, which we also present in this manuscript.