2 citations · 3 across the 8 of their papers we have counts for
8 papers
Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space
Houjun Liu, Shikhar Murty, Christopher D. Manning +1
Current approaches for scaling inference-time compute in transformers train them to emit explicit chain-of-thought tokens before producing an answer. While these methods are powerf…
WebDS: An End-to-End Benchmark for Web-based Data Science
Ethan Hsu, Hong Meng Yam, Ines Bouissou +9
Many real-world data science tasks involve complex web-based interactions: finding appropriate data available on the internet, synthesizing multimodal data from different locations…
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…
Sneaking Syntax into Transformer Language Models with Tree Regularization
Ananjan Nandi, Christopher D. Manning, Shikhar Murty
While compositional accounts of human language understanding are based on a hierarchical tree-like process, neural models like transformers lack a direct inductive bias for such tr…
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Julie Kallini, Shikhar Murty, Christopher D. Manning +2
Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across dif…
NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild
Shikhar Murty, Hao Zhu, Dzmitry Bahdanau +1
We introduce NNetNav, a method for unsupervised interaction with websites that generates synthetic demonstrations for training browser agents. Given any website, NNetNav produces t…
BAGEL: Bootstrapping Agents by Guiding Exploration with Language
Shikhar Murty, Christopher Manning, Peter Shaw +2
Following natural language instructions by executing actions in digital environments (e.g. web-browsers and REST APIs) is a challenging task for language model (LM) agents. Unfortu…
Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
Shikhar Murty, Pratyusha Sharma, Jacob Andreas +1
Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently…