3 citations · 4 across the 9 of their papers we have counts for
9 papers
ARC Is a Vision Problem!
Keya Hu, Ali Cy, Linlu Qiu +5
The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a…
Sequential-Parallel Duality in Prefix Scannable Models
Morris Yau, Sharut Gupta, Valerie Engelmayer +3
Modern neural sequence models are designed to meet the dual mandate of parallelizable training and fast sequential inference. Recent developments have given rise to various models,…
Breakpoint: Scalable evaluation of system-level reasoning in LLM code agents
Kaivalya Hariharan, Uzay Girit, Atticus Wang +1
Benchmarks for large language models (LLMs) have predominantly assessed short-horizon, localized reasoning. Existing long-horizon suites (e.g. SWE-bench) rely on manually curated i…
MisinfoEval: Generative AI in the Era of "Alternative Facts"
Saadia Gabriel, Liang Lyu, James Siderius +3
The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address…
Algorithmic Capabilities of Random Transformers
Ziqian Zhong, Jacob Andreas
Trained transformer models have been found to implement interpretable procedures for tasks like arithmetic and associative recall, but little is understood about how the circuits t…
Language Modeling with Editable External Knowledge
Belinda Z. Li, Emmy Liu, Alexis Ross +3
When the world changes, so does the text that humans write about it. How do we build language models that can be easily updated to reflect these changes? One popular approach is re…
An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models
Emmy Liu, Graham Neubig, Jacob Andreas
Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in natural language; in few-shot pr…
Evaluating the Utility of Model Explanations for Model Development
Shawn Im, Jacob Andreas, Yilun Zhou
One of the motivations for explainable AI is to allow humans to make better and more informed decisions regarding the use and deployment of AI models. But careful evaluations are n…
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell +1
Neural language models (LMs) can be used to evaluate the truth of factual statements in two ways: they can be either queried for statement probabilities, or probed for internal rep…