70 citations · 223 across the 41 of their papers we have counts for
43 papers · 1 filter
From UNDRR Reports to Event Records: Schema-Constrained LLM Extraction of Georeferenced Disasters
Camilla Andreozzi, Phuong-Anh Nguyen-Le, Zhijing Jin +1
Disaster-risk-reduction archives describe hazard events in prose that databases such as EM-DAT (Delforge et al., 2025) cannot ingest directly. We present an LLM pipeline that gener…
When Do Language Models Endorse Limitations on Human Rights Principles?
Keenan Samway, Nicole Miu Takagi, Rada Mihalcea +4
As Large Language Models (LLMs) increasingly mediate global information access with the potential to shape public discourse, their alignment with universal human rights principles…
Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders
Abir Harrasse, Florent Draye, Punya Syon Pandey +2
Multilingual Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear. Do they form shared multilingual representat…
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
Chenfei Xiong, Jingwei Ni, Yu Fan +10
We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic…
Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models
David Guzman Piedrahita, Irene Strauss, Bernhard Schölkopf +2
As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior…
Improving Large Language Model Safety with Contrastive Representation Learning
Samuel Simko, Mrinmaya Sachan, Bernhard Schölkopf +1
Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to…
Can Theoretical Physics Research Benefit from Language Agents?
Sirui Lu, Zhijing Jin, Terry Jingchen Zhang +3
Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains inadequate. While current models show competence in…
Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards
Punya Syon Pandey, Samuel Simko, Kellin Pelrine +1
As large language models (LLMs) gain popularity, their vulnerability to adversarial attacks emerges as a primary concern. While fine-tuning models on domain-specific datasets is of…
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities
Aly M. Kassem, Bernhard Schölkopf, Zhijing Jin
Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance by dynamically assigning queries to the most appropriate mod…
DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal
Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh +2
Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In softwar…
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…
Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis
David F. Jenny, Yann Billeter, Mrinmaya Sachan +2
The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding the prevalence of bias in these models and its mitigation. Yet, as exemplified by both re…
CausalCite: A Causal Formulation of Paper Citations
Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian +4
Citation count of a paper is a commonly used proxy for evaluating the significance of a paper in the scientific community. Yet citation measures are widely criticized for failing t…
Can Large Language Models Infer Causation from Correlation?
Zhijing Jin, Jiarui Liu, Zhiheng Lyu +5
Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in…