Search Results for author:
Found 9 papers, 6 papers with code
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model.
Ranked #49 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #3 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
Self-Supervised Multi-Modal Sequential Recommendation
To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation.
WizardLM: Empowering Large Language Models to Follow Complex Instructions
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
Multimodal Dialogue Response Generation
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification
Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available.
