Search Results
Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived.
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning.
Ranked #143 on
Visual Question Answering
on MM-Vet
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
Extending Llama-3's Context Ten-Fold Overnight
We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning.
Chinese-Vicuna: A Chinese Instruction-following Llama-based Model
Chinese-Vicuna is an open-source, resource-efficient language model designed to bridge the gap in Chinese instruction-following capabilities by fine-tuning Meta's LLaMA architecture using Low-Rank Adaptation (LoRA).
Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients
To address these limitations, we introduce Q-Galore, a novel approach that substantially reduces memory usage by combining quantization and low-rank projection, surpassing the benefits of GaLore.
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
PiSSA shares the same architecture as LoRA, but initializes the adaptor matrices $A$ and $B$ with the principal components of the original matrix $W$, and put the remaining components into a residual matrix $W^{res} \in \mathbb{R}^{m \times n}$ which is frozen during fine-tuning.
CoLLaVO: Crayon Large Language and Vision mOdel
Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on vision language (VL) tasks.
Ranked #108 on
Visual Question Answering
on MM-Vet
Spectrum: Targeted Training on Signal to Noise Ratio
Efficiently post-training large language models remains a challenging task due to the vast computational resources required.
