λ-WASP
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- UT Austin
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 June 2007
- Authors
- YW Wong, R Mooney
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language Structure Modeling
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"Table 1 summarizes the results at the end of the learning curves (792 training examples for λWASP, WASP and SCISSOR, 600 for Z&C)"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Citations
- 383
"The resulting parser is shown to be the bestperforming system so far in a database query domain" "The result is a robust semantic parser for predicate logic, and it is the best-performing system so far in the GEOQUERY domain."
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
λ-WASP was published by UT Austin, in the country recorded as United States of America, during June 2007. It comes out of an organisation categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language Structure Modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The reason it appears in this catalogue at all: sOTA improvement.
Answers
λ-WASP — common questions
λ-WASP— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language Structure Modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
λ-WASP— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
λ-WASP— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
λ-WASP— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
λ-WASP— who created it?
It was published by UT Austin, based in United States of America, an organisation categorised as academia.
λ-WASP— when was it released?
It was published in June 2007. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.