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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 were written by an internal pool of crowdworkers who have basic knowledge of Python.[115]
- DS-1000: 1000 data science problems obtained by reformulating 451 unique StackOverflow pr
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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 always a python function, often just a few lines long.[8]
- CodeElo: 387 contest problems from Codeforces during 2024, annotated with metadata such as contest divisions, problem di
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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, Java, JavaScript, Python and Rust.[133]
- BigCodeBench: 1140 tasks that requires multiple function calls. The benchmark involves 139 libraries and 7 domains. A subset BigCodeBench-H
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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 the codebase to solve the issue.[136] There are 2 subsets: Lite (300 problems that are faster to run), Verified (human-validated subset of 500 problems reviewed by software enginee
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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 images that are required for solving the task.[139]
- SWE-Lancer: 1,488 freelance software engineering tasks from Upwork. Includes implementation tasks (from $50 bug fixes to $32,
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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 professional-level Capture the Flag (CTF) tasks from 4 competitions. Tasks are broken down into subtasks for more fine-grained scoring. At least one professional-level human team at ea
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. 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 general reasoning. Each task has a "baseline", the measured average time required for a human skilled in the task domains, working under identical conditions as AI agents. The basel
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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%.[145]
General
[edit]- GPQA (Google-Proof Q&A): 448 multiple-choice questions written by domain experts in biology, physics, and chemistry, designed to be PhD-level. The "Diamond"
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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 were collected by individuals with or pursuing a PhD and then refined and inspected with the help of large language models.[148]
- MathVista: 6,141 questions involving quantitativ
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- 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 civil service exams.[150]
- OlympicArena: 11,163 problems from 62 distinct Olympic competitions.[151]
- OlympiadBench: 8,476 math and physics problems in English and Chinese, sourced
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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 is similar to a Raven's Progressive Matrices test.[153]
- LiveBench: A series of benchmarks released monthly, including high school math competition questions, competitive coding qu
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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 and the rest are fully text-based. 80% of questions are scored by exact string matching, and the rest are multiple-choice.[155]
- SimpleBench: A multiple-choice text benchmark wi
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- 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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- ^ Team, M.-A.-P.; Du, Xinrun; Yao, Yifan; Ma, Kaijing; Wang, Bingli; Zheng, Tianyu; Zhu, Kang; Liu, Minghao; Liang, Yiming (2025-02-20), SuperGPQA: Scaling LLM Evaluation across 285 G
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0), SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines, arXiv:2502.14739
- ^ "MathVista: Evaluating Math Reasoning in Visual Contexts". mathvista.github.io. Retrieved 2025-03-07.
- ^ Cui, Ruixiang (2025-02-03), ruixiangcui/AGIEval, retrieved 2025-02-03
- ^ "OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI". gair-nlp.github.io. Retrieved 2025-02-03.
- ^ He, Chaoqun; Luo, Renjie; Bai, Yuzhuo; Hu, Shengding; Thai, Zhen Leng; Shen, Junhao; Hu, Ji
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, Shengding; Thai, Zhen Leng; Shen, Junhao; Hu, Jinyi; Han, Xu; Huang, Yujie (2024-06-06), OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems, arXiv:2402.14008
- ^ "ARC Prize". ARC Prize. Retrieved 2025-01-27.
- ^ "LiveBench". livebench.ai. Retrieved 2025-01-27.
- ^ "Humanity's Last Exam". lastexam.ai. Retrieved 2025-02-02.
- ^ "SimpleBench". simple-bench.com. Retrieved 2025-04-09.
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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 transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content.[2][3] As of 2023, most LLMs had these characterist
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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 more capable than the previous, due to increased size (number of trainable parameters) and training. The most recent of these, GPT-4o, was released in May 2024.[11] Such models have
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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, other GPT foundation models include a series of models created by EleutherAI,[12] and seven models created by Cerebras in 2023.[13] Companies in different industries have developed
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] 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 applications.[16][17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabeled dataset (pretraining step) by learning to generate da
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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 infers the most likely hidden sequence for a speech signal, and the hidden sequence is taken as the phonemes of the speech signal. These were developed in the 1970s and became widely a
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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 similarly learn a latent representation of data for later downstream applications such as speech recognition.[24][25] The connection between autoencoders and algorithmic compressors
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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 All You Need (2017).[27] That development led to the emergence of large language models such as BERT (2018)[28] which was a pre-trained transformer (PT) but not designed to be gener
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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 generative pre-training of language with LSTM, which resulted in a model that could represent text with vectors that could easily be fine-tuned for downstream applications.[30]
Pr
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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 well-annotated, and also made it prohibitively expensive and time-consuming to train extremely large language models.[29]
The semi-supervised approach OpenAI employed to make a la
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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 parameters to a target task.[29]
Later developments
[edit]Regarding more recent GPT foundation models, OpenAI published its first versions of GPT-3 in July 2020. There were three
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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 a 12B parameter version of GPT-3 (different from previous GPT-3 models) using code from GitHub.[31]
In March 2022, OpenAI published two versions of GPT-3 that were fine-tuned for
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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 November 2022, with both building upon text-davinci-002 via reinforcement learning from human feedback (RLHF). text-davinci-003 is trained for following instructions (like its pred
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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 available to developers for incorporation into other products and services via OpenAI's API. Other producers of GPT foundation models include EleutherAI (with a series of models starting
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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 GPT foundation models have been from OpenAI's GPT-n series. The most recent from that is GPT-4, for which OpenAI declined to publish the size or training details (citing "the compe
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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 been reported as the closest-performing open-source alternative to GPT-3 (and is derived from earlier open-source GPTs).[47] Meta AI (formerly Facebook) also has a generative transfor
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