5 citations · 14 across the 26 of their papers we have counts for
12 papers · 1 filter
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
Hengyu Fu, Tianyu Guo, Zixuan Wang +5
Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT). However, many questions require t…
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers
Yixiao Huang, Hanlin Zhu, Tianyu Guo +5
Large language models (LLMs) can acquire new knowledge through fine-tuning, but this process exhibits a puzzling duality: models can generalize remarkably from new facts, yet are a…
How Do LLMs Perform Two-Hop Reasoning in Context?
Tianyu Guo, Hanlin Zhu, Ruiqi Zhang +4
``Socrates is human. All humans are mortal. Therefore, Socrates is mortal.'' This form of argument illustrates a typical pattern of two-hop reasoning. Formally, two-hop reasoning r…
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
DiJia Su, Hanlin Zhu, Yingchen Xu +3
Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data, where the step-by-step thought process is explicitly outlined by text token…
EmbedLLM: Learning Compact Representations of Large Language Models
Richard Zhuang, Tianhao Wu, Zhaojin Wen +3
With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream, tasks has become increasi…
Thinking LLMs: General Instruction Following with Thought Generation
Tianhao Wu, Janice Lan, Weizhe Yuan +3
LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework they lack the basic…
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
Tianhao Wu, Weizhe Yuan, Olga Golovneva +5
Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding m…
Toxicity Detection for Free
Zhanhao Hu, Julien Piet, Geng Zhao +2
Current LLMs are generally aligned to follow safety requirements and tend to refuse toxic prompts. However, LLMs can fail to refuse toxic prompts or be overcautious and refuse beni…
Toward a Theory of Tokenization in LLMs
Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran
While there has been a large body of research attempting to circumvent tokenization for language modeling (Clark et al., 2022; Xue et al., 2022), the current consensus is that it i…
Efficient Prompt Caching via Embedding Similarity
Hanlin Zhu, Banghua Zhu, Jiantao Jiao
Large language models (LLMs) have achieved huge success in numerous natural language process (NLP) tasks. However, it faces the challenge of significant resource consumption during…
End-to-end Story Plot Generator
Hanlin Zhu, Andrew Cohen, Danqing Wang +4
Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words. We study the problem of automatic generation of story…