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https://en.wikipedia.org/wiki/Language_model_benchmark#67 | benchmark LLMs, to prevent contamination.[128]
Programming
[edit]- APPS: 10,000 problems from Codewars, AtCoder, Kattis, and Codeforces.[129]
- MBPP (Mostly Basic Programming Problems): 974 short Python functions designed to be solved by entry-level programmers. Each comes with a text description and unit tests. They w... |
https://en.wikipedia.org/wiki/Language_model_benchmark#68 | ained by reformulating 451 unique StackOverflow problems, requiring the use of 7 Python libraries, such as NumPy and Pandas. The resposes are scored by running test cases and comparing outputs, and checking for the presence/absence of specific APIs or keywords.[130][131]
- HumanEval: 164 problems where the solution is ... |
https://en.wikipedia.org/wiki/Language_model_benchmark#69 | ith metadata such as contest divisions, problem difficulty ratings, and problem algorithm tags. Benchmarking is run by directly submitting to Codeforces, resulting in an Elo rating. Limited to 8 submissions per problem.[132]
- Aider Polyglot: 225 of the hardest coding exercises from Exercism, in languages of C++, Go, J... |
https://en.wikipedia.org/wiki/Language_model_benchmark#70 | 9 libraries and 7 domains. A subset BigCodeBench-Hard involves just a 148-task subset of the full benchmark.[134][135]
- SWE-bench: 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Given a codebase and an issue, the task is to edit ... |
https://en.wikipedia.org/wiki/Language_model_benchmark#71 | ubset of 500 problems reviewed by software engineers).[137]
- Multi-SWE-bench: 1,632 problems across 7 languages: Java, TypeScript, JavaScript, Go, Rust, C, and C++. Similar to SWE-bench.[138]
- SWE-bench Multimodal: a variant of SWE-bench, with 619 task instances from 17 popular JavaScript repositories, each featuring... |
https://en.wikipedia.org/wiki/Language_model_benchmark#72 | s implementation tasks (from $50 bug fixes to $32,000 feature implementations) and managerial tasks, where the model must choose between technical implementation proposals.[140][141]
- KernelBench: 250 PyTorch machine learning tasks, for which a CUDA kernel must be written.[142]
- Cybench (cybersecurity bench): 40 prof... |
https://en.wikipedia.org/wiki/Language_model_benchmark#73 | . At least one professional-level human team at each competition was able to solve each of the tasks. The time it took the fastest team to solve each task ranged from 2 minutes to 25 hours.[143]
- HCAST (Human-Calibrated Autonomy Software Tasks): 189 tasks in machine learning, cybersecurity, software engineering, and g... |
https://en.wikipedia.org/wiki/Language_model_benchmark#74 | under identical conditions as AI agents. The baseline ranges from 1 minute to 8+ hours.[144]
- PaperBench: 8,316 individually gradable tasks that would be necessary for replicating 20 Spotlight and Oral papers from ICML 2024 from scratch. The human baseline of ML PhDs (best of 3 attempts) at 48 hours of effort is 41.4%... |
https://en.wikipedia.org/wiki/Language_model_benchmark#75 | hemistry, designed to be PhD-level. The "Diamond" subset contains the 198 hardest questions in it.[146] OpenAI found that human experts achieve an average score of 69.7% on the Diamond subset.[147]
- SuperGPQA: 26,529 multiple-choice questions collected by domain experts in 285 graduate-level disciplines. The questions... |
https://en.wikipedia.org/wiki/Language_model_benchmark#76 | - MathVista: 6,141 questions involving quantitative reasoning that requires reading a picture to solve.[149]
- AGIEval: questions from 20 official, public, and high-standard admission and qualification exams, such as SAT, Gaokao, law school admission tests, math competitions, lawyer qualification tests, and national ci... |
https://en.wikipedia.org/wiki/Language_model_benchmark#77 | d physics problems in English and Chinese, sourced from International Olympiads, Chinese Olympiads, and Gaokao.[152]
- ARC-AGI (Abstraction and Reasoning Corpus for Artificial General Intelligence): Given three pairs of before-and-after diagrams of applying a rule, apply the same rule to the fourth before-diagram. It i... |
https://en.wikipedia.org/wiki/Language_model_benchmark#78 | math competition questions, competitive coding questions, logic puzzles, and other tasks.[154]
- Humanity's Last Exam: 3,000 multimodal questions across over a hundred academic subjects, with a held-out private dataset left unreleased to prevent contamination. 10% of questions requires both image and text comprehension... |
https://en.wikipedia.org/wiki/Language_model_benchmark#79 | - SimpleBench: A multiple-choice text benchmark with over 200 questions covering spatio-temporal reasoning, social intelligence, and linguistic adversarial robustness (or trick questions). It is designed to test "everyday human reasoning".[156]
See also
[edit]External links
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https://en.wikipedia.org/wiki/Language_model_benchmark#82 | z, Fernando; Metzler, Donald; Vinyals, Oriol (2021-07-14), The Benchmark Lottery, arXiv:2107.07002
- ^ DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (2025-01-22), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, a... |
https://en.wikipedia.org/wiki/Language_model_benchmark#83 | wards, Harri; Burda, Yuri; Joseph, Nicholas (2021-07-14), Evaluating Large Language Models Trained on Code, arXiv:2107.03374
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https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#0 | Generative pre-trained transformer
A generative pre-trained transformer (GPT) is a type of large language model (LLM)[1][2][3] and a prominent framework for generative artificial intelligence.[4][5] It is an artificial neural network that is used in natural language processing by machines.[6] It is based on the transfo... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#1 | 2][3] As of 2023, most LLMs had these characteristics[7] and are sometimes referred to broadly as GPTs.[8]
The first GPT was introduced in 2018 by OpenAI.[9] OpenAI has released significant GPT foundation models that have been sequentially numbered, to comprise its "GPT-n" series.[10] Each of these was significantly mo... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#2 | o, was released in May 2024.[11] Such models have been the basis for their more task-specific GPT systems, including models fine-tuned for instruction following—which in turn power the ChatGPT chatbot service.[1]
The term "GPT" is also used in the names and descriptions of such models developed by others. For example, ... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#3 | ] Companies in different industries have developed task-specific GPTs in their respective fields, such as Salesforce's "EinsteinGPT" (for CRM)[14] and Bloomberg's "BloombergGPT" (for finance).[15]
History
[edit]Initial developments
[edit]Generative pretraining (GP) was a long-established concept in machine learning app... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#4 | aset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labeled dataset.[18]
There were three main types of early GP. The hidden Markov models learn a generative model of sequences for downstream applications. For example, in speech recognition, a trained HMM infe... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#5 | se were developed in the 1970s and became widely applied in speech recognition in the 1980s.[19][20]
The compressors learn to compress data such as images and textual sequences, and the compressed data serves as a good representation for downstream applications such as facial recognition.[21][22][23] The autoencoders s... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#6 | n between autoencoders and algorithmic compressors was noted in 1993.[26]
During the 2010s, the problem of machine translation was solved[citation needed] by recurrent neural networks, with attention mechanism added. This was optimized into the transformer architecture, published by Google researchers in Attention Is A... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#7 | ined transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series.[29]
Previously in 2017, some of the authors who would later work on GPT-1 worked on... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#8 | be fine-tuned for downstream applications.[30]
Prior to transformer-based architectures, the best-performing neural NLP (natural language processing) models commonly employed supervised learning from large amounts of manually-labeled data. The reliance on supervised learning limited their use on datasets that were not ... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#9 | i-supervised approach OpenAI employed to make a large-scale generative system—and was first to do with a transformer model—involved two stages: an unsupervised generative "pretraining" stage to set initial parameters using a language modeling objective, and a supervised discriminative "fine-tuning" stage to adapt these... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#10 | versions of GPT-3 in July 2020. There were three models, with 1B, 6.7B, 175B parameters, respectively named babbage, curie, and davinci (giving initials B, C, and D).[citation needed]
In July 2021, OpenAI published Codex, a task-specific GPT model targeted for programming applications. This was developed by fine-tuning... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#11 | hed two versions of GPT-3 that were fine-tuned for instruction-following (instruction-tuned), named davinci-instruct-beta (175B) and text-davinci-001,[32] and then started beta testing code-davinci-002.[33] text-davinci-002 was instruction-tuned from code-davinci-002. Both text-davinci-003 and ChatGPT were released in ... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#12 | trained for following instructions (like its predecessors), whereas ChatGPT is further trained for conversational interaction with a human user.[34][35]
OpenAI's most recent GPT foundation model, GPT-4, was released on March 14, 2023. It can be accessed directly by users via a premium version of ChatGPT, and is availab... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#13 | clude EleutherAI (with a series of models starting in March 2021)[12] and Cerebras (with seven models released in March 2023).[13]
Foundation models
[edit]A foundation model is an AI model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks.[36][37]
Thus far, the most notable ... |
https://en.wikipedia.org/wiki/Generative_pre-trained_transformer#14 | sh the size or training details (citing "the competitive landscape and the safety implications of large-scale models").[38]
Other such models include Google's PaLM, a broad foundation model that has been compared to GPT-3 and has been made available to developers via an API,[45][46] and Together's GPT-JT, which has bee... |
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