34 citations · 93 across the 19 of their papers we have counts for
19 papers
An empirical study on the limitation of Transformers in program trace generation
Simeng Sun
We study Transformers on the task \emph{program trace generation} (PTG), where models produce step-by-step execution traces for synthetic programs. Unlike existing algorithmic prob…
SWAN-GPT: An Efficient and Scalable Approach for Long-Context Language Modeling
Krishna C. Puvvada, Faisal Ladhak, Santiago Akle Serrano +8
We present a decoder-only Transformer architecture that robustly generalizes to sequence lengths substantially longer than those seen during training. Our model, SWAN-GPT, interlea…
L0-Reasoning Bench: Evaluating Procedural Correctness in Language Models via Simple Program Execution
Simeng Sun, Cheng-Ping Hsieh, Faisal Ladhak +3
Complex reasoning tasks often rely on the ability to consistently and accurately apply simple rules across incremental steps, a foundational capability which we term "level-0" reas…
How much do contextualized representations encode long-range context?
Simeng Sun, Cheng-Ping Hsieh
We analyze contextual representations in neural autoregressive language models, emphasizing long-range contexts that span several thousand tokens. Our methodology employs a perturb…
nGPT: Normalized Transformer with Representation Learning on the Hypersphere
Ilya Loshchilov, Cheng-Ping Hsieh, Simeng Sun +1
We propose a novel neural network architecture, the normalized Transformer (nGPT) with representation learning on the hypersphere. In nGPT, all vectors forming the embeddings, MLP,…
Suri: Multi-constraint Instruction Following for Long-form Text Generation
Chau Minh Pham, Simeng Sun, Mohit Iyyer
Existing research on instruction following largely focuses on tasks with simple instructions and short responses. In this work, we explore multi-constraint instruction following fo…