1.6k citations · 12.4k across the 394 of their papers we have counts for
5 papers · 2 filters
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning
Shaobo Cui, Zhijing Jin, Bernhard Schölkopf +1
Understanding commonsense causality is a unique mark of intelligence for humans. It helps people understand the principles of the real world better and benefits the decision-making…
Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries
Roberto Ceraolo, Dmitrii Kharlapenko, Ahmad Khan +6
Recent progress in Large Language Model (LLM) technology has changed our role in interacting with these models. Instead of primarily testing these models with questions we already…
Implicit Personalization in Language Models: A Systematic Study
Zhijing Jin, Nils Heil, Jiarui Liu +5
Implicit Personalization (IP) is a phenomenon of language models inferring a user's background from the implicit cues in the input prompts and tailoring the response based on this…
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents
Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner +3
As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulat…
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Francesco Ortu, Zhijing Jin, Diego Doimo +3
Interpretability research aims to bridge the gap between empirical success and our scientific understanding of the inner workings of large language models (LLMs). However, most exi…