Search Results for author:
Found 16 papers, 11 papers with code
Data Selection via Optimal Control for Language Models
In our experiments, we adopt PDS to select data from CommmonCrawl and show that the PDS-selected corpus accelerates the learning of LMs and constantly boosts their performance on a wide range of downstream tasks across various model sizes.
Towards Optimal Learning of Language Models
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
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 science and mathematics.
Kosmos-2: Grounding Multimodal Large Language Models to the World
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 world.
Ranked #13 on
Visual Question Answering
on ViP-Bench
Language Is Not All You Need: Aligning Perception with Language Models
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
We comprehensively compare the behaviors of in-context learning and explicit finetuning on real tasks to provide empirical evidence that supports our understanding.
Optimizing Prompts for Text-to-Image Generation
Instead of laborious human engineering, we propose prompt adaptation, a general framework that automatically adapts original user input to model-preferred prompts.
Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters.
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
In this paper, we move towards combining large parametric models with non-parametric prototypical networks.
Language Models are General-Purpose Interfaces
Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.
Ranked #2 on
Image Captioning
on nocaps val
Prototypical Calibration for Few-shot Learning of Language Models
In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations.
Learning to Sample Replacements for ELECTRA Pre-Training
Moreover, we propose to use a focal loss for the generator in order to relieve oversampling of correct tokens as replacements.
Knowledge Neurons in Pretrained Transformers
In this paper, we present preliminary studies on how factual knowledge is stored in pretrained Transformers by introducing the concept of knowledge neurons.
Investigating Learning Dynamics of BERT Fine-Tuning
The recently introduced pre-trained language model BERT advances the state-of-the-art on many NLP tasks through the fine-tuning approach, but few studies investigate how the fine-tuning process improves the model performance on downstream tasks.
Self-Attention Attribution: Interpreting Information Interactions Inside Transformer
The great success of Transformer-based models benefits from the powerful multi-head self-attention mechanism, which learns token dependencies and encodes contextual information from the input.
Visualizing and Understanding the Effectiveness of BERT
Language model pre-training, such as BERT, has achieved remarkable results in many NLP tasks.
