584 citations · 688 across the 17 of their papers we have counts for
22 papers
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…
How Language Model Hallucinations Can Snowball
Muru Zhang, Ofir Press, William Merrill +2
A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs,…
A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworks
William Merrill, Nikolaos Tsilivis, Aman Shukla
Grokking is a phenomenon where a model trained on an algorithmic task first overfits but, then, after a large amount of additional training, undergoes a phase transition to general…
Transparency Helps Reveal When Language Models Learn Meaning
Zhaofeng Wu, William Merrill, Hao Peng +2
Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acqui…
A Logic for Expressing Log-Precision Transformers
William Merrill, Ashish Sabharwal
One way to interpret the reasoning power of transformer-based language models is to describe the types of logical rules they can resolve over some input text. Recently, Chiang et a…
Entailment Semantics Can Be Extracted from an Ideal Language Model
William Merrill, Alex Warstadt, Tal Linzen
Language models are often trained on text alone, without additional grounding. There is debate as to how much of natural language semantics can be inferred from such a procedure. W…