70 citations · 223 across the 41 of their papers we have counts for
12 papers · 1 filter
Causal Responsibility Attribution for Human-AI Collaboration
Yahang Qi, Bernhard Schölkopf, Zhijing Jin
As Artificial Intelligence (AI) systems increasingly influence decision-making across various fields, the need to attribute responsibility for undesirable outcomes has become essen…
Automatic Generation of Model and Data Cards: A Step Towards Responsible AI
Jiarui Liu, Wenkai Li, Zhijing Jin +1
In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardi…
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…
On Affine Homotopy between Language Encoders
Robin SM Chan, Reda Boumasmoud, Anej Svete +8
Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis:…
Analyzing the Role of Semantic Representations in the Era of Large Language Models
Zhijing Jin, Yuen Chen, Fernando Gonzalez +5
Traditionally, natural language processing (NLP) models often use a rich set of features created by linguistic expertise, such as semantic representations. However, in the era of l…
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…
Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection
Abhinav Lalwani, Tasha Kim, Lovish Chopra +3
Translating natural language into formal language such as First-Order Logic (FOL) is a foundational challenge in NLP with wide-ranging applications in automated reasoning, misinfor…
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…
Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis
Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez +3
Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review. Given a review and the sentiment associated with it, this work formulates SA a…
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…
CLadder: Assessing Causal Reasoning in Language Models
Zhijing Jin, Yuen Chen, Felix Leeb +8
The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason…