Search Results for author: Abulhair Saparov

Found 4 papers, 4 papers with code

Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

1 code implementation3 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).

Mathematical Reasoning Question Answering

Towards General Natural Language Understanding with Probabilistic Worldbuilding

2 code implementations6 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.

Natural Language Understanding Question Answering +1

Jelly Bean World: A Testbed for Never-Ending Learning

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.

BIG-bench Machine Learning Navigate

A Probabilistic Generative Grammar for Semantic Parsing

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.

Natural Language Understanding Semantic Parsing +1

Cannot find the paper you are looking for? You can Submit a new open access paper.