26 citations · 30 across the 10 of their papers we have counts for
10 papers
tse_tick: A Python Library for Parsing and Querying Nikkei NEEDS Tick Data from the Tokyo Stock Exchange
Kazumi Li, Masataka Hayashi, Teruo Nakatsuma +1
Tick-level trade-and-quote data for the Tokyo Stock Exchange is distributed through the Nikkei NEEDS service as thousands of zipped CSV archives spanning four data types with era-d…
Scale-Dependent Collective Adaptation in Self-Amending LLM Societies: A Cross-Family Study of Emergent Governance
Kazuya Horibe, Masaomi Hatakeyama, Gen Masumoto +2
We study group decision-making in artificial societies where the rules of play are themselves subject to collective amendment. Using the self-amending game Nomic, we compare multip…
Capabilities Ain't All You Need: Measuring Propensities in AI
Daniel Romero-Alvarado, Fernando Martínez-Plumed, Lorenzo Pacchiardi +11
AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasingly applied. Yet propensities - the te…
From Human-Level AI Tales to AI Leveling Human Scales
Peter Romero, Fernando Martínez-Plumed, Zachary R. Tidler +11
Comparing AI models to "human level" is often misleading when benchmark scores are incommensurate or human baselines are drawn from a narrow population. To address this, we propose…
Psychometric Personality Shaping Modulates Capabilities and Safety in Language Models
Stephen Fitz, Peter Romero, Steven Basart +2
Large Language Models increasingly mediate high-stakes interactions, intensifying research on their capabilities and safety. While recent work has shown that LLMs exhibit consisten…
Do GPT Language Models Suffer From Split Personality Disorder? The Advent Of Substrate-Free Psychometrics
Peter Romero, Stephen Fitz, Teruo Nakatsuma
Previous research on emergence in large language models shows these display apparent human-like abilities and psychological latent traits. However, results are partly contradicting…
Hidden Holes: topological aspects of language models
Stephen Fitz, Peter Romero, Jiyan Jonas Schneider
We explore the topology of representation manifolds arising in autoregressive neural language models trained on raw text data. In order to study their properties, we introduce tool…
Personality Traits in Large Language Models
Greg Serapio-García, Mustafa Safdari, Clément Crepy +6
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs in…
Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors
Ahmed Izzidien, David Stillwell
Fairness is a principal social value that can be observed in civilisations around the world. A manifestation of this is in social agreements, often described in texts, such as cont…
The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning
Ahmed Izzidien, David Stillwell
In this paper we present a natural language programming framework to consider how the fairness of acts can be measured. For the purposes of the paper, a fair act is defined as one…