73 citations · 129 across the 15 of their papers we have counts for
17 papers
Web2Wiki: Characterizing Wikipedia Linking Across the Web
Veniamin Veselovsky, Tiziano Piccardi, Ashton Anderson +2
Wikipedia is one of the most visited websites globally, yet its role beyond its own platform remains largely unexplored. In this paper, we present the first large-scale analysis of…
Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models
Liyi Zhang, Veniamin Veselovsky, R. Thomas McCoy +1
Large language models (LLMs) sometimes fail to respond appropriately to deterministic tasks -- such as counting or forming acronyms -- because the implicit prior distribution they…
Localized Cultural Knowledge is Conserved and Controllable in Large Language Models
Veniamin Veselovsky, Berke Argin, Benedikt Stroebl +5
Just as humans display language patterns influenced by their native tongue when speaking new languages, LLMs often default to English-centric responses even when generating in othe…
Levels of Analysis for Large Language Models
Alexander Y. Ku, Declan Campbell, Xuechunzi Bai +10
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to…
What is a Number, That a Large Language Model May Know It?
Raja Marjieh, Veniamin Veselovsky, Thomas L. Griffiths +1
Numbers are a basic part of how humans represent and describe the world around them. As a consequence, learning effective representations of numbers is critical for the success of…
Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers
Clément Dumas, Chris Wendler, Veniamin Veselovsky +2
A central question in multilingual language modeling is whether large language models (LLMs) develop a universal concept representation, disentangled from specific languages. In th…
Self-Recognition in Language Models
Tim R. Davidson, Viacheslav Surkov, Veniamin Veselovsky +3
A rapidly growing number of applications rely on a small set of closed-source language models (LMs). This dependency might introduce novel security risks if LMs develop self-recogn…
The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates
Giuseppe Russo Latona, Manoel Horta Ribeiro, Tim R. Davidson +2
Journals and conferences worry that peer reviews assisted by artificial intelligence (AI), in particular, large language models (LLMs), may negatively influence the validity and fa…
Do Llamas Work in English? On the Latent Language of Multilingual Transformers
Chris Wendler, Veniamin Veselovsky, Giovanni Monea +1
We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language -- a question of key importance for understan…
Evaluating Language Model Agency through Negotiations
Tim R. Davidson, Veniamin Veselovsky, Martin Josifoski +4
We introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings o…
Capturing Dynamics in Online Public Discourse: A Case Study of Universal Basic Income Discussions on Reddit
Rachel Kim, Veniamin Veselovsky, Ashton Anderson
Societal change is often driven by shifts in public opinion. As citizens evolve in their norms, beliefs, and values, public policies change too. While traditional opinion polling a…
Prevalence and prevention of large language model use in crowd work
Veniamin Veselovsky, Manoel Horta Ribeiro, Philip Cozzolino +3
We show that the use of large language models (LLMs) is prevalent among crowd workers, and that targeted mitigation strategies can significantly reduce, but not eliminate, LLM use.…
Tube2Vec: Social and Semantic Embeddings of YouTube Channels
Léopaul Boesinger, Manoel Horta Ribeiro, Veniamin Veselovsky +1
Research using YouTube data often explores social and semantic dimensions of channels and videos. Typically, analyses rely on laborious manual annotation of content and content cre…
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks
Veniamin Veselovsky, Manoel Horta Ribeiro, Robert West
Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the…
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora +3
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a perva…
Reddit in the Time of COVID
Veniamin Veselovsky, Ashton Anderson
When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online pl…
The Amplification Paradox in Recommender Systems
Manoel Horta Ribeiro, Veniamin Veselovsky, Robert West
Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studi…