activity
20222025
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 706 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025★ 2 cited

Persona Features Control Emergent Misalignment

Miles Wang, Tom Dupré la Tour, Olivia Watkins +8

Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that…

cs.CL2024★ 3 cited

Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance

Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3

The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…

cs.CL2024★ 2 cited

modeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models

Nathan A. Chi, Teodor Malchev, Riley Kong +5

We introduce modeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of…

cs.CL2024★ 1 cited

LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages

Andrew M. Bean, Simi Hellsten, Harry Mayne +5

In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in large language models. Using challenging Linguistic Olympiad puzzles, we eval…

cs.AI2024★ 3 cited

Premise Order Matters in Reasoning with Large Language Models

Xinyun Chen, Ryan A. Chi, Xuezhi Wang +1

Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a frailty: LLMs are surpr…

cs.CL2024★ 2 cited

When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…

cs.CL2022★ 128 cited

Holistic Evaluation of Language Models

Percy Liang, Rishi Bommasani, Tony Lee +47

Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…

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…