16 citations · 36 across the 9 of their papers we have counts for
8 papers · 1 filter
ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
Hongjin Su, Shizhe Diao, Ximing Lu +13
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and comp…
BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation
Bo Pang, Hanze Dong, Jiacheng Xu +3
Large language models (LLMs), such as o1 from OpenAI, have demonstrated remarkable reasoning capabilities. o1 generates a long chain-of-thought (LongCoT) before answering a questio…
Neural Architecture Search on Efficient Transformers and Beyond
Zexiang Liu, Dong Li, Kaiyue Lu +4
Recently, numerous efficient Transformers have been proposed to reduce the quadratic computational complexity of standard Transformers caused by the Softmax attention. However, mos…
Massive-scale Decoding for Text Generation using Lattices
Jiacheng Xu, Siddhartha Reddy Jonnalagadda, Greg Durrett
Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options. We present a sear…
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Aditya Gupta, Jiacheng Xu, Shyam Upadhyay +2
Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation. This is largely due to the lack of datasets containing disfluencies. In this pape…
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
Jiacheng Xu, Greg Durrett
Despite the prominence of neural abstractive summarization models, we know little about how they actually form summaries and how to understand where their decisions come from. We p…