2.2k citations · 7.1k across the 72 of their papers we have counts for
24 papers · 1 filter
Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning
Shaokun Zhang, Yi Dong, Jieyu Zhang +6
Enabling large language models with external tools has become a pivotal strategy for extending their functionality beyond text space. To enhance LLMs' tool-calling abilities, previ…
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
Boxin Wang, Wei Ping, Lawrence McAfee +4
Pretraining auto-regressive large language models~(LLMs) with retrieval demonstrates better perplexity and factual accuracy by leveraging external databases. However, the size of e…
Retrieval meets Long Context Large Language Models
Peng Xu, Wei Ping, Xianchao Wu +7
Extending the context window of large language models (LLMs) is getting popular recently, while the solution of augmenting LLMs with retrieval has existed for years. The natural qu…
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
Jie Huang, Wei Ping, Peng Xu +3
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models a…
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Boxin Wang, Wei Ping, Peng Xu +9
Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task ac…
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models
Shrimai Prabhumoye, Mostofa Patwary, Mohammad Shoeybi +1
Pretrained large language models have become indispensable for solving various natural language processing (NLP) tasks. However, safely deploying them in real world applications is…