Search Results for author:
Found 6 papers, 4 papers with code
Fine-tuning Large Language Models with Sequential Instructions
Targeting the scarcity of sequential instructions in present-day data, we propose sequential instruction tuning, a simple yet effective strategy to automatically augment instruction tuning data and equip LLMs with the ability to execute multiple sequential instructions.
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
The best candidate is finally selected from this set based on the BLEURT score.
Meta-learning For Vision-and-language Cross-lingual Transfer
Current pre-trained vison-language models (PVLMs) achieve excellent performance on a range of multi-modal datasets.
Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models
To this end, we propose a novel method called CoS (Chain-of-Symbol Prompting) that represents the complex environments with condensed symbolic spatial representations during the chained intermediate thinking steps.
Improving User Controlled Table-To-Text Generation Robustness
In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval
In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.
