activity
20192025
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 613 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs

Mantas Mazeika, Xuwang Yin, Rishub Tamirisa +8

As AIs rapidly advance and become more agentic, the risk they pose is governed not only by their capabilities but increasingly by their propensities, including goals and values. Tr…

cs.LG2025★ 23 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1168

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…

cs.LG2024★ 13 cited

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Nathaniel Li, Alexander Pan, Anjali Gopal +54

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and che…

cs.CL2023★ 1 cited

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding

Steven H. Wang, Antoine Scardigli, Leonard Tang +7

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address…

cs.CL2022★ 565 cited

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…

cs.LG2021★ 10 cited

Temperature as Uncertainty in Contrastive Learning

Oliver Zhang, Mike Wu, Jasmine Bayrooti +1

Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…

cs.LG2019★ 1 cited

Optimal Transport Based Generative Autoencoders

Oliver Zhang, Ruei-Sung Lin, Yuchuan Gou

The field of deep generative modeling is dominated by generative adversarial networks (GANs). However, the training of GANs often lacks stability, fails to converge, and suffers fr…