Evaluating Large Language Models Trained on Code.
Mark ChenJerry TworekHeewoo JunQiming YuanHenrique Ponde de Oliveira PintoJared KaplanHarrison EdwardsYuri BurdaNicholas JosephGreg BrockmanAlex RayRaul PuriGretchen KruegerMichael PetrovHeidy KhlaafGirish SastryPamela MishkinBrooke ChanScott GrayNick RyderMikhail PavlovAlethea PowerLukasz KaiserMohammad BavarianClemens WinterPhilippe TilletFelipe Petroski SuchDave CummingsMatthias PlappertFotios ChantzisElizabeth BarnesAriel Herbert-VossWilliam Hebgen GussAlex NicholAlex PainoNikolas TezakJie TangIgor BabuschkinSuchir BalajiShantanu JainWilliam SaundersChristopher HesseAndrew N. CarrJan LeikeJoshua AchiamVedant MisraEvan MorikawaAlec RadfordMatthew KnightMiles BrundageMira MuratiKatie MayerPeter WelinderBob McGrewDario AmodeiSam McCandlishIlya SutskeverWojciech ZarembaPublished in: CoRR (2021)
Keyphrases
- language model
- language modeling
- probabilistic model
- n gram
- speech recognition
- document retrieval
- query expansion
- information retrieval
- test collection
- language modelling
- query terms
- language models for information retrieval
- statistical language models
- relevance model
- retrieval model
- context sensitive
- smoothing methods
- pseudo relevance feedback
- translation model
- document ranking
- training set
- ad hoc information retrieval
- vector space model
- passage retrieval
- document length
- retrieval effectiveness
- query specific
- term dependencies
- search engine