Search Results for author: Graham Todd

Found 5 papers, 1 papers with code

Unsupervised Anomaly Detection in Parole Hearings using Language Models

no code implementations EMNLP (NLP+CSS) 2020 Graham Todd, Catalin Voss, Jenny Hong

We present quantitative analysis of the results and note that our method has identified some important cases for review.

Unsupervised Anomaly Detection

Missed Connections: Lateral Thinking Puzzles for Large Language Models

no code implementations17 Apr 2024 Graham Todd, Tim Merino, Sam Earle, Julian Togelius

This is because the four categories ascend in complexity, with the most challenging category often requiring thinking about words in uncommon ways or as parts of larger phrases.

Sentence Sentence Embedding +1

Large Language Models and Games: A Survey and Roadmap

no code implementations28 Feb 2024 Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis

Recent years have seen an explosive increase in research on large language models (LLMs), and accompanying public engagement on the topic.

ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games

1 code implementation24 May 2023 Ruoyao Wang, Graham Todd, Eric Yuan, Ziang Xiao, Marc-Alexandre Côté, Peter Jansen

In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks.

Code Generation Common Sense Reasoning +2

Level Generation Through Large Language Models

no code implementations11 Feb 2023 Graham Todd, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green, Julian Togelius

Large Language Models (LLMs) are powerful tools, capable of leveraging their training on natural language to write stories, generate code, and answer questions.

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