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[HTML][HTML] A survey of GPT-3 family large language models including ChatGPT and GPT-4
KS Kalyan - Natural Language Processing Journal, 2024 - Elsevier
Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
Datasets for large language models: A comprehensive survey
This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …
[PDF][PDF] A survey of large language models
Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …
of language intelligence by machine. Language is essentially a complex, intricate system of …
Chameleon: Plug-and-play compositional reasoning with large language models
Large language models (LLMs) have achieved remarkable progress in solving various
natural language processing tasks due to emergent reasoning abilities. However, LLMs …
natural language processing tasks due to emergent reasoning abilities. However, LLMs …
Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit impressive
problem-solving skills in many tasks and domains, but their ability in mathematical …
problem-solving skills in many tasks and domains, but their ability in mathematical …
Mammoth2: Scaling instructions from the web
Instruction tuning improves the reasoning abilities of large language models (LLMs), with
data quality and scalability being the crucial factors. Most instruction tuning data come from …
data quality and scalability being the crucial factors. Most instruction tuning data come from …
Rest-mcts*: Llm self-training via process reward guided tree search
D Zhang, S Zhoubian, Z Hu, Y Yue… - Advances in Neural …, 2025 - proceedings.neurips.cc
Recent methodologies in LLM self-training mostly rely on LLM generating responses and
filtering those with correct output answers as training data. This approach often yields a low …
filtering those with correct output answers as training data. This approach often yields a low …
HelpSteer 2: Open-source dataset for training top-performing reward models
Z Wang, Y Dong, O Delalleau, J Zeng… - Advances in …, 2025 - proceedings.neurips.cc
High-quality preference datasets are essential for training reward models that can effectively
guide large language models (LLMs) in generating high-quality responses aligned with …
guide large language models (LLMs) in generating high-quality responses aligned with …
Mmlu-pro: A more robust and challenging multi-task language understanding benchmark
In the age of large-scale language models, benchmarks like the Massive Multitask
Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI …
Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI …
Scientific large language models: A survey on biological & chemical domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …
natural language comprehension, representing a significant stride toward artificial general …