164 citations · 501 across the 21 of their papers we have counts for
21 papers
Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
Yao Tang, Li Dong, Yaru Hao +3
Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast,…
The Era of Agentic Organization: Learning to Organize with Language Models
Zewen Chi, Li Dong, Qingxiu Dong +4
We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intel…
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts
Di Zhang, Xun Wu, Shaohan Huang +6
Recent advances in reinforcement learning (RL) have substantially improved the training of large-scale language models, leading to significant gains in generation quality and reaso…
Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective
Siwei Wang, Yifei Shen, Haoran Sun +5
Recent reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs), yet the theoretical basis for their effectiveness…
Geometric-Mean Policy Optimization
Yuzhong Zhao, Yue Liu, Junpeng Liu +9
Group Relative Policy Optimization (GRPO) has significantly enhanced the reasoning capability of large language models by optimizing the arithmetic mean of token-level rewards. Unf…
On-Policy RL with Optimal Reward Baseline
Yaru Hao, Li Dong, Xun Wu +3
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement…
Data Selection via Optimal Control for Language Models
Yuxian Gu, Li Dong, Hongning Wang +4
This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage. We formulate data selection as a gene…
Towards Optimal Learning of Language Models
Yuxian Gu, Li Dong, Yaru Hao +3
This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance.…
Large Language Model for Science: A Study on P vs. NP
Qingxiu Dong, Li Dong, Ke Xu +4
In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer sci…
Kosmos-2: Grounding Multimodal Large Language Models to the World
Zhiliang Peng, Wenhui Wang, Li Dong +4
We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual…
Language Is Not All You Need: Aligning Perception with Language Models
Shaohan Huang, Li Dong, Wenhui Wang +15
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multim…
Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Yaru Hao, Yutao Sun, Li Dong +3
Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters. However, convent…
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
Damai Dai, Yutao Sun, Li Dong +4
Large pretrained language models have shown surprising in-context learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input…
Optimizing Prompts for Text-to-Image Generation
Yaru Hao, Zewen Chi, Li Dong +1
Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of…
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
Yiqiao Jin, Xiting Wang, Yaru Hao +2
In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for…
Language Models are General-Purpose Interfaces
Yaru Hao, Haoyu Song, Li Dong +5
Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architectur…
Prototypical Calibration for Few-shot Learning of Language Models
Zhixiong Han, Yaru Hao, Li Dong +2
In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical…
Learning to Sample Replacements for ELECTRA Pre-Training
Yaru Hao, Li Dong, Hangbo Bao +2
ELECTRA pretrains a discriminator to detect replaced tokens, where the replacements are sampled from a generator trained with masked language modeling. Despite the compelling perfo…
Knowledge Neurons in Pretrained Transformers
Damai Dai, Li Dong, Yaru Hao +3
Large-scale pretrained language models are surprisingly good at recalling factual knowledge presented in the training corpus. In this paper, we present preliminary studies on how f…
Self-Attention Attribution: Interpreting Information Interactions Inside Transformer
Yaru Hao, Li Dong, Furu Wei +1
The great success of Transformer-based models benefits from the powerful multi-head self-attention mechanism, which learns token dependencies and encodes contextual information fro…
Visualizing and Understanding the Effectiveness of BERT
Yaru Hao, Li Dong, Furu Wei +1
Language model pre-training, such as BERT, has achieved remarkable results in many NLP tasks. However, it is unclear why the pre-training-then-fine-tuning paradigm can improve perf…