70 citations · 223 across the 41 of their papers we have counts for
11 papers · 1 filter
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…
Test of Time: Rethinking Temporal Signal of Benchmark Contamination
Terry Jingchen Zhang, Gopal Dev, Ning Wang +8
Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may…
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…
Causality can systematically address the monsters under the bench(marks)
Felix Leeb, Zhijing Jin, Bernhard Schölkopf
Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever mo…
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal method…