most citedStars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias

1 citations · 1 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CL2026

Verbalizing Activation Differences with Natural Language Autoencoders

Andrea Blasi Núñez, Gianluca Barmina, Federico Torrielli +3

Activation verbalizers translate the internal representations of large language models into human-readable text. The recently proposed natural language autoencoders (NLAs) learn th…

cs.CL2026

Selecting The Most Informative Tokens in Natural Language Autoencoders

Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez +4

Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly. Which positions should an aud…

cs.MA2026

The Energy Society: A Simulation Environment for Studying Agent Cooperation under Survival Pressure

Lucas Bergholdt Hansen, Federico Torrielli, Filippo Tonini +1

LLM-based agents are increasingly deployed in multi-agent environments whose incentives can shape their behavior. We introduce The Energy Society, a minimal survival economy for st…

cs.AI2026

The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment

Filippo Tonini, Federico Torrielli, Anton Danholt Lautrup +3

As AI systems built from multiple language-model agents become more common, they are increasingly used to make decisions together: discussing, negotiating, and acting on shared tas…

cs.CL2026

PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models

Gianluca Barmina, Federico Torrielli, Sven Harms +7

Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpf…

cs.CL2026

Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion

Stine Lyngsø Beltoft, William Brach, Federico Torrielli +5

Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding huma…

cs.CL2026

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment

William Brach, Federico Torrielli, Stine Lyngsø Beltoft +3

Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious safety concerns. With the aim of…

cs.CL2026

Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals

Federico Torrielli, Peter Schneider-Kamp, Lukas Galke Poech

An activation oracle is a language model trained to convert the activations of another model into natural language. However, it is so far unclear to what extent these verbalization…

cs.CY2024★ 1 cited

Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias

Federico Torrielli

This paper investigates the challenges associated with bias, toxicity, unreliability, and lack of robustness in large language models (LLMs) such as ChatGPT. It emphasizes that the…