86 citations · 260 across the 16 of their papers we have counts for
18 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
Self-Adapting Language Models
Adam Zweiger, Jyothish Pari, Han Guo +3
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs…
Is Active Persona Inference Necessary for Aligning Small Models to Personal Preferences?
Zilu Tang, Afra Feyza Akyürek, Ekin Akyürek +1
A prominent issue in aligning language models (LMs) to personalized preferences is underspecification -- the lack of information from users about their preferences. A popular trend…
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Ekin Akyürek, Mehul Damani, Adam Zweiger +5
Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks even when given a small number…
Learning Linear Attention in Polynomial Time
Morris Yau, Ekin Akyürek, Jiayuan Mao +3
Previous research has explored the computational expressivity of Transformer models in simulating Boolean circuits or Turing machines. However, the learnability of these simulators…
Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models
Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin +17
The ability to build and reason about models of the world is essential for situated language understanding. But evaluating world modeling capabilities in modern AI systems -- espec…
In-Context Language Learning: Architectures and Algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim +1
Large-scale neural language models exhibit a remarkable capacity for in-context learning (ICL): they can infer novel functions from datasets provided as input. Most of our current…
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Afra Feyza Akyürek, Ekin Akyürek, Leshem Choshen +2
While language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims, these generally do not reflect a globally coherent, manipu…
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
Zhaofeng Wu, Linlu Qiu, Alexis Ross +6
The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills. Are these skills general and tra…
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan +4
Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing langua…
Backpropagation through Back Substitution with a Backslash
Alan Edelman, Ekin Akyurek, Yuyang Wang
We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written ``backslash'' or Gaussian elimination on triangu…
What learning algorithm is in-context learning? Investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas +2
Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples $(x, f(…
Towards Tracing Factual Knowledge in Language Models Back to the Training Data
Ekin Akyürek, Tolga Bolukbasi, Frederick Liu +4
Language models (LMs) have been shown to memorize a great deal of factual knowledge contained in their training data. But when an LM generates an assertion, it is often difficult t…
Pre-Trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton +11
Language model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an…
Subspace Regularizers for Few-Shot Class Incremental Learning
Afra Feyza Akyürek, Ekin Akyürek, Derry Tanti Wijaya +1
Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge…
Lexicon Learning for Few-Shot Neural Sequence Modeling
Ekin Akyürek, Jacob Andreas
Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural…
Learning to Recombine and Resample Data for Compositional Generalization
Ekin Akyürek, Afra Feyza Akyürek, Jacob Andreas
Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositiona…
Morphological analysis using a sequence decoder
Ekin Akyürek, Erenay Dayanık, Deniz Yuret
We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and…