activity
20152025
most citedLambdaNet: Probabilistic Type Inference using Graph Neural Networks

47 citations · 125 across the 23 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CL2023

A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation

Jarad Forristal, Niloofar Mireshghallah, Greg Durrett +1

Recent work has shown that energy-based language modeling is an effective framework for controllable text generation because it enables flexible integration of arbitrary discrimina…

cs.CL2023

QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing

Yating Wu, Ritika Mangla, Greg Durrett +1

Questions Under Discussion (QUD) is a versatile linguistic framework in which discourse progresses as continuously asking questions and answering them. Automatic parsing of a disco…

cs.CL2023

MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Zayne Sprague, Xi Ye, Kaj Bostrom +2

While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to rea…

cs.CL2023

A Long Way to Go: Investigating Length Correlations in RLHF

Prasann Singhal, Tanya Goyal, Jiacheng Xu +1

Great success has been reported using Reinforcement Learning from Human Feedback (RLHF) to align large language models, with open preference datasets enabling wider experimentation…

cs.CL2023

X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs

Juan Diego Rodriguez, Katrin Erk, Greg Durrett

Understanding when two pieces of text convey the same information is a goal touching many subproblems in NLP, including textual entailment and fact-checking. This problem becomes m…

cs.CL2023

Deductive Additivity for Planning of Natural Language Proofs

Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri +1

Current natural language systems designed for multi-step claim validation typically operate in two phases: retrieve a set of relevant premise statements using heuristics (planning)…