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List Of Language Model Benchmarks
Language model benchmarks are standardized tests designed to evaluate the performance of language models on various natural language processing tasks. These tests are intended for comparing different models' capabilities in areas such as Natural language understanding, language understanding, Natural language generation, generation, and Reasoning language model, reasoning. Benchmarks generally consist of a Data set, dataset and corresponding Evaluation, evaluation metrics. The dataset provides text samples and annotations, while the metrics measure a model's performance on tasks like question answering, text classification, and machine translation. These benchmarks are developed and maintained by academic institutions, research organizations, and industry players to track progress in the field. Overview Types Benchmarks may be described by the following adjectives, not mutually exclusive: * Classical: These tasks are studied in natural language processing, even before the ad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Model
A language model is a model of the human brain's ability to produce natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation,Andreas, Jacob, Andreas Vlachos, and Stephen Clark (2013)"Semantic parsing as machine translation". Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word ''n''-gram language model. History Noam Chomsky did pioneering work on lan ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linguistic Data Consortium
The Linguistic Data Consortium is an open consortium of universities, companies and government research laboratories. It creates, collects and distributes speech and text databases, lexicons, and other resources for linguistics research and development purposes. The University of Pennsylvania is the LDC's host institution. The LDC was founded in 1992 with a grant from the US Defense Advanced Research Projects Agency (DARPA), and is partly supported by grant IRI-9528587 from the Information and Intelligent Systems division of the National Science Foundation. The director of LDC is Mark Liberman. It subsumed the previous ACL Data Collection Initiative. Part of the motivation was to support the benchmark-oriented methodology of DARPA's Human Language Technology program. Previously, John R. Pierce directed the committee that produced the ALPAC report (1966), which caused a severe decrease in funding for linguistic AI for about 10 years. Later, Charles Wayne restarted funding in sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cherry Picking
Cherry picking, suppressing evidence, or the fallacy of incomplete evidence is the act of pointing to individual cases or data that seem to confirm a particular position while ignoring a significant portion of related and similar cases or data that may contradict that position. Cherry picking may be committed intentionally or unintentionally. The term is based on the perceived process of harvesting fruit, such as cherries. The picker would be expected to select only the ripest and healthiest fruits. An observer who sees only the selected fruit may thus wrongly conclude that most, or even all, of the tree's fruit is in a likewise good condition. This can also give a false impression of the quality of the fruit (since it is only a sample and is not a representative sample). A concept sometimes confused with cherry picking is the idea of gathering only the fruit that is easy to harvest, while ignoring other fruit that is higher up on the tree and thus more difficult to obtain (see ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Goodhart's Law
Goodhart's law is an adage that has been stated as, "When a measure becomes a target, it ceases to be a good measure". It is named after British economist Charles Goodhart, who is credited with expressing the core idea of the adage in a 1975 article on monetary policy in the United Kingdom: It was used to criticize the British Thatcher government for trying to conduct monetary policy on the basis of targets for broad and narrow money, but the law reflects a much more general phenomenon. Priority and background Numerous concepts are related to this idea, at least one of which predates Goodhart's statement. Notably, Campbell's law likely has precedence, as Jeff Rodamar has argued, since various formulations date to 1969. Other academics had similar insights at the time. Jerome Ravetz's 1971 book '' Scientific Knowledge and Its Social Problems'' also predates Goodhart, though it does not formulate the same law. He discusses how systems in general can be gamed, focuses on cases ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Leakage (machine Learning)
In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which would not be expected to be available at prediction time, causing the predictive scores (metrics) to overestimate the model's utility when run in a production environment. Leakage is often subtle and indirect, making it hard to detect and eliminate. Leakage can cause a statistician or modeler to select a suboptimal model, which could be outperformed by a leakage-free model. Leakage modes Leakage can occur in many steps in the machine learning process. The leakage causes can be sub-classified into two possible sources of leakage for a model: features and training examples. Feature leakage Feature or column-wise leakage is caused by the inclusion of columns which are one of the following: a duplicate label, a proxy for the label, or the label itself. These features, known as anachronisms, will not be available when the model is u ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Formal Proof
In logic and mathematics, a formal proof or derivation is a finite sequence of sentences (known as well-formed formulas when relating to formal language), each of which is an axiom, an assumption, or follows from the preceding sentences in the sequence, according to the rule of inference. It differs from a natural language argument in that it is rigorous, unambiguous and mechanically verifiable. If the set of assumptions is empty, then the last sentence in a formal proof is called a theorem of the formal system. The notion of theorem is generally effective, but there may be no method by which we can reliably find proof of a given sentence or determine that none exists. The concepts of Fitch-style proof, sequent calculus and natural deduction are generalizations of the concept of proof. The theorem is a syntactic consequence of all the well-formed formulas preceding it in the proof. For a well-formed formula to qualify as part of a proof, it must be the result of applying a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LEPOR
LEPOR (Length Penalty, Precision, n-gram Position difference Penalty and Recall) is an automatic language independent machine translation evaluation metric with tunable parameters and reinforced factors. Background Since IBM proposed and realized the system of BLEU as the automatic metric for Machine Translation (MT) evaluation, many other methods have been proposed to revise or improve it, such as TER, METEOR, etc. However, there exist some problems in the traditional automatic evaluation metrics. Some metrics perform well on certain languages but weak on other languages, which is usually called as a language bias problem. Some metrics rely on a lot of language features or linguistic information, which makes it difficult for other researchers to repeat the experiments. LEPOR is an automatic evaluation metric that tries to address some of the existing problems.Han et al. (2012) LEPOR is designed with augmented factors and the corresponding tunable parameters to address the langua ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Error Rate
Word error rate (WER) is a common metric of the performance of a speech recognition or machine translation system. The WER metric typically ranges from 0 to 1, where 0 indicates that the compared pieces of text are exactly identical, and 1 (or larger) indicates that they are completely different with no similarity. This way, a WER of 0.8 means that there is an 80% error rate for compared sentences. The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
NIST (metric)
NIST is a method for evaluating the quality of text which has been translated using machine translation. Its name comes from the US National Institute of Standards and Technology. It is based on the BLEU metric, but with some alterations. Where BLEU simply calculates n-gram precision adding equal weight to each one, NIST also calculates how informative a particular n-gram is. That is to say when a correct n-gram is found, the rarer that n-gram is, the more weight it will be given. For example, if the bigram "on the" is correctly matched, it will receive lower weight than the correct matching of bigram "interesting calculations", as this is less likely to occur. NIST also differs from BLEU in its calculation of the brevity penalty insofar as small variations in translation length do not impact the overall score as much. See also * BLEU * F-Measure * METEOR A meteor, known colloquially as a shooting star, is a glowing streak of a small body (usually meteoroid) going ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
METEOR
A meteor, known colloquially as a shooting star, is a glowing streak of a small body (usually meteoroid) going through Earth's atmosphere, after being heated to incandescence by collisions with air molecules in the upper atmosphere, creating a streak of light via its rapid motion and sometimes also by shedding glowing material in its wake. Although a meteor may seem to be a few thousand feet from the Earth, meteors typically occur in the mesosphere at altitudes from . The root word ''meteor'' comes from the Ancient Greek, Greek ''meteĊros'', meaning "high in the air". Millions of meteors occur in Earth's atmosphere daily. Most meteoroids that cause meteors are about the size of a grain of sand, i.e. they are usually millimeter-sized or smaller. Meteoroid sizes can be calculated from their mass and density which, in turn, can be estimated from the observed meteor trajectory in the upper atmosphere. Meteors may occur in meteor shower, showers, which arise when Earth passes throu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ROUGE (metric)
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. ROUGE metrics range between 0 and 1, with higher scores indicating higher similarity between the automatically produced summary and the reference. Metrics The following five evaluation metrics are available. *ROUGE-N: Overlap of n-grams between the system and reference summaries. **ROUGE-1 refers to the overlap of ''unigrams'' ''(each word)'' between the system and reference summaries. **ROUGE-2 refers to the overlap of ''bigrams'' between the system and reference summaries. *ROUGE-L: Longest Common Subsequence (LCS) based statistics. Longest common subsequence problem takes into account sentence-level structure similarity ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
F-score
In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. Precision is also known as positive predictive value, and recall is also known as sensitivity in diagnostic binary classification. The F1 score is the harmonic mean of the precision and recall. It thus symmetrically represents both precision and recall in one metric. The more generic F_\beta score applies additional weights, valuing one of precision or recall more than the other. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
