Large language models for data annotation: A survey
Data annotation generally refers to the labeling or generating of raw data with relevant
information, which could be used for improving the efficacy of machine learning models. The …
information, which could be used for improving the efficacy of machine learning models. The …
Continual learning of large language models: A comprehensive survey
The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …
general datasets has sparked numerous research directions and applications. One such …
A survey of large language models
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …
The llama 3 herd of models
Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …
presents a new set of foundation models, called Llama 3. It is a herd of language models …
Qwen technical report
Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …
enabling natural language processing tasks that were previously thought to be exclusive to …
Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?
The remarkable progress of Multi-modal Large Language Models (MLLMs) has gained
unparalleled attention. However, their capabilities in visual math problem-solving remain …
unparalleled attention. However, their capabilities in visual math problem-solving remain …
Metamath: Bootstrap your own mathematical questions for large language models
Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …
and exhibited excellent problem-solving ability. Despite the great success, most existing …
Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Mathematical reasoning poses a significant challenge for language models due to its
complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which …
complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which …
Math-shepherd: Verify and reinforce llms step-by-step without human annotations
In this paper, we present an innovative process-oriented math process reward model called
Math-shepherd, which assigns a reward score to each step of math problem solutions. The …
Math-shepherd, which assigns a reward score to each step of math problem solutions. The …
Tora: A tool-integrated reasoning agent for mathematical problem solving
Large language models have made significant progress in various language tasks, yet they
still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool …
still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool …