text: 343,171,1400516024437411843,1188224435364327424,Belinda Li,"Do neural language models (trained on text alone!) construct representations of meaning? In a new #ACL2021NLP paper, we find that LM representations implicitly model *entities and situations* as they evolve through a discourse. 1/ <LINK> <LINK> These representations roughly correspond to linguistic models of dynamic semantics---they update as new sentences are added to the discourse, to reflect changes to the underlying state. 2/ <LINK> Specifically, we train BART and T5 models on text transcripts in two datasets (Alchemy and Textworld). We find a linear probe can map encoder representations of text to the truth values of propositions about world that the text describes. 3/ <LINK> Furthermore, the representations are local: in Alchemy, the probe can tell the final state of an entity by looking at the first appearance of an entity in a discourse. 4/ <LINK> Finally, the representations can be manipulated to affect downstream generation: by replacing a small set of LM encodings, we can induce the decoder to generate text consistent with the *modified* state representation. 5/ <LINK> Of course, LMs still make lots of semantic errors, and our experiments only look at a tiny slice of semantics. The datasets we test on are far simpler than, e.g., those featured in the thought experiments of Bender&Koller. Even here, probe accuracies are far from perfect. 6/ However, these experiments suggest that it’s possible to learn noisy, approximate representations of some aspects of meaning from text alone. 7/ This work would not have been possible without my amazing co-authors @Maxwell_Nye and @jacobandreas! Code is available at: <LINK> 8/8",https://arxiv.org/abs/2106.00737,"Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity's current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data. Code and data are available at this https URL . ",Implicit Representations of Meaning in Neural Language Models,8,"['Do neural language models (trained on text alone!) construct representations of meaning? In a new #ACL2021NLP paper, we find that LM representations implicitly model *entities and situations* as they evolve through a discourse. 1/\n<LINK> <LINK>', 'These representations roughly correspond to linguistic models of dynamic semantics---they update as new sentences are added to the discourse, to reflect changes to the underlying state. 2/ https://t.co/4AcxdqHN3m', 'Specifically, we train BART and T5 models on text transcripts in two datasets (Alchemy and Textworld). We find a linear probe can map encoder representations of text to the truth values of propositions about world that the text describes. 3/ https://t.co/JNlutxCjuJ', 'Furthermore, the representations are local: in Alchemy, the probe can tell the final state of an entity by looking at the first appearance of an entity in a discourse. 4/ https://t.co/8RkpTF1Npq', 'Finally, the representations can be manipulated to affect downstream generation: by replacing a small set of LM encodings, we can induce the decoder to generate text consistent with the *modified* state representation. 5/ https://t.co/IbXRWcssdv', 'Of course, LMs still make lots of semantic errors, and our experiments only look at a tiny slice of semantics. The datasets we test on are far simpler than, e.g., those featured in the thought experiments of Bender&amp;Koller. Even here, probe accuracies are far from perfect. 6/', 'However, these experiments suggest that it’s possible to learn noisy, approximate representations of some aspects of meaning from text alone. 7/', 'This work would not have been possible without my amazing co-authors @Maxwell_Nye and @jacobandreas!\n\nCode is available at: https://t.co/TW2vcC2Igp\n\n8/8']",21,06,1651