48 citations · 69 across the 9 of their papers we have counts for
9 papers
$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li +19
As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured…
Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale
Ran Tian, Ankur P. Parikh
We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks. It can be viewed as an Adam optimizer with theoretically supported, adaptive lear…
Simple Recurrence Improves Masked Language Models
Tao Lei, Ran Tian, Jasmijn Bastings +1
In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into t…
Shatter: An Efficient Transformer Encoder with Single-Headed Self-Attention and Relative Sequence Partitioning
Ran Tian, Joshua Maynez, Ankur P. Parikh
The highly popular Transformer architecture, based on self-attention, is the foundation of large pretrained models such as BERT, that have become an enduring paradigm in NLP. While…
Local Additivity Based Data Augmentation for Semi-supervised NER
Jiaao Chen, Zhenghui Wang, Ran Tian +2
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the…
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Ran Tian, Shashi Narayan, Thibault Sellam +1
We address the issue of hallucination in data-to-text generation, i.e., reducing the generation of text that is unsupported by the source. We conjecture that hallucination can be c…