1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2025
Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations
Ananth Agarwal, Jasper Jian, Christopher D. Manning +1
Large Language Models (LLMs) exhibit a robust mastery of syntax when processing and generating text. While this suggests internalized understanding of hierarchical syntax and depen…
cs.CL2022★ 1 cited
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
Eric Mitchell, Joseph J. Noh, Siyan Li +5
While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering…