activity
20172026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 979 across the 85 of their papers we have counts for

collaborators
Showing 2025Show all

24 papers · 1 filter

cs.CL2025

Social Story Frames: Contextual Reasoning about Narrative Intent and Reception

Joel Mire, Maria Antoniak, Steven R. Wilson +4

Reading stories evokes rich interpretive, affective, and evaluative responses, such as inferences about narrative intent or judgments about characters. Yet, computational models of…

cs.CL2025★ 2 cited

Olmo 3

Team Olmo, :, Allyson Ettinger +66

We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…

cs.CL2025★ 1 cited

Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)

Liwei Jiang, Yuanjun Chai, Margaret Li +7

Language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human thought through repeated exposur…

cs.CL2025

EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences

Kshitish Ghate, Andy Liu, Devansh Jain +5

As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We…

cs.CL2025

Fluid Language Model Benchmarking

Valentin Hofmann, David Heineman, Ian Magnusson +7

Language model (LM) benchmarking faces several challenges: comprehensive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality…

cs.AI2025

The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains

Scott Geng, Hamish Ivison, Chun-Liang Li +4

Improvements in language models are often driven by improving the quality of the data we train them on, which can be limiting when strong supervision is scarce. In this work, we sh…