From schuler.77 at osu.edu Thu Jan 11 10:59:49 2024 From: schuler.77 at osu.edu (Schuler, William) Date: Thu, 11 Jan 2024 15:59:49 +0000 Subject: [CaCL] Cognitive and Computational Approaches to Language (CaCL) Discussion Group: Thursdays 12:45 in Oxley 122 Message-ID: Hello all, The reading group on Cognitive and Computational Approaches to Language (CaCL) will meet Thursdays at 12:45 starting this week in Oxley 122. The link is accessible via the Carmen site, and requires name.# authentication. If you do not want to enroll, but would still like to attend, please send me email to either make you a Carmen guest or send you a Zoom invitation. Topics include: broad-coverage computational models of sentence processing in human memory, computational models of human memory, statistical modeling for human linguistic performance data, neural sentence processing models, Bayesian and neural grammar induction models, experimental techniques in neurolinguistics and brain imaging, and much more. The first session will be an organizational meeting, during which we will vote on papers to discuss (feel free to suggest any papers you'd like to discuss!). We will read papers and give software tutorials and practice talks all semester. Please join us! Also, if you are a student this summer, and you are planning to attend, please enroll for 1-3 credits: CaCL is listed as "LING 7890.12?. You can also sign up for the CaCL mailing list at https://lists.osu.edu/mailman/listinfo/cacl, which continues to serve the regular CaCL reading group throughout the academic year. Hope to see you there! William -------------- next part -------------- An HTML attachment was scrubbed... URL: From oh.531 at buckeyemail.osu.edu Thu Jan 11 13:39:58 2024 From: oh.531 at buckeyemail.osu.edu (Oh, Byung-Doh) Date: Thu, 11 Jan 2024 18:39:58 +0000 Subject: [CaCL] 1/18: Reinforced Self-Training (ReST) for Language Modeling Message-ID: Hi everyone, Next week, we'll discuss the following paper; I might follow up with additional resources that may be helpful for fully appreciating this paper as well. Reinforced Self-Training (ReST) for Language Modeling https://arxiv.org/pdf/2308.08998.pdf Reinforcement learning from human feedback (RLHF) can improve the quality of large language model?s (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner. Best, Byung-Doh ================= Byung-Doh Oh (he/him/his) Ph.D. Candidate Department of Linguistics The Ohio State University -------------- next part -------------- An HTML attachment was scrubbed... URL: From clark.3664 at buckeyemail.osu.edu Thu Jan 18 14:35:32 2024 From: clark.3664 at buckeyemail.osu.edu (Clark, Christian) Date: Thu, 18 Jan 2024 19:35:32 +0000 Subject: [CaCL] Reading for 1/25 Message-ID: Hi CaCL members, Our reading for next week will be Pi?ango 2023. Solving the elusiveness of word meanings: two arguments for a continuous meaning space for language Solving the elusiveness of word meanings: two arguments for a continuous meaning space for language https://www.frontiersin.org/articles/10.3389/frai.2023.1025293/full I explore the hypothesis that the experience of meaning discreteness when we think about the ?meaning? of a word is a ?communicative? illusion. The illusion is created by processing-contextual constraints that impose disambiguation on the semantic input making salient a specific interpretation within a conceptual space that is otherwise continuous. It is this salience that we experience as discreteness. The understanding of word meaning as non-discrete raises the question of what is context; what are the mechanisms of constraint that it imposes and what is the nature of the conceptual space with which pronunciations (i.e., visual/oral signs) associate themselves. I address these questions by leveraging an algebraic continuous system for word meaning that is itself constrained by two fundamental parameters: control-asymmetry and connectedness. I evaluate this model by meeting two challenges to word meaning discreteness (1) cases where the same pronunciation is associated with multiple senses that are nonetheless interdependent, e.g., English ?smoke,? and (2) cases where the same pronunciation is associated with a family of meanings, minimally distinct from each other organized as a ?cline,? e.g., English ?have.? These cases are not marginal?they are ubiquitous in languages across the world. Any model that captures them is accounting for the meaning system for language. At the heart of the argumentation is the demonstration of how the parameterized space naturally organizes these kinds of cases without appeal for further categorization or segmentation of any kind. From this, I conclude that discreteness in word meaning is epiphenomenal: it is the experience of salience produced by contextual constraints. And that this is possible because, by and large, every time that we become consciously aware of the conceptual structure associated with a pronunciation, i.e., its meaning, we do so under real-time processing conditions which are biased toward producing a specific interpretation in reference to a specific situation in the world. Supporting it is a parameterized space that gives rise to lexico-conceptual representations: generalized algebraic structures necessary for the identification, processing, and encoding of an individual's understanding of the world. ---- Christian Clark Ph.D. Student Department of Linguistics The Ohio State University -------------- next part -------------- An HTML attachment was scrubbed... URL: From schuler.77 at osu.edu Thu Jan 25 10:22:30 2024 From: schuler.77 at osu.edu (Schuler, William) Date: Thu, 25 Jan 2024 15:22:30 +0000 Subject: [CaCL] meeting in Oxley 102 this week (Re: Reading for 1/25) In-Reply-To: References: Message-ID: Hi all, This week CaCL will meet in Oxley 102 because of NACLO, wm From: CaCL on behalf of Clark, Christian via CaCL Date: Thursday, January 18, 2024 at 2:35 PM To: Schuler, William via CaCL Subject: [CaCL] Reading for 1/25 Hi CaCL members, Our reading for next week will be Pi?ango 2023. Solving the elusiveness of word meanings: two arguments for a continuous meaning space for language Solving the elusiveness of word meanings: two arguments for a continuous meaning space for language https://www.frontiersin.org/articles/10.3389/frai.2023.1025293/full I explore the hypothesis that the experience of meaning discreteness when we think about the ?meaning? of a word is a ?communicative? illusion. The illusion is created by processing-contextual constraints that impose disambiguation on the semantic input making salient a specific interpretation within a conceptual space that is otherwise continuous. It is this salience that we experience as discreteness. The understanding of word meaning as non-discrete raises the question of what is context; what are the mechanisms of constraint that it imposes and what is the nature of the conceptual space with which pronunciations (i.e., visual/oral signs) associate themselves. I address these questions by leveraging an algebraic continuous system for word meaning that is itself constrained by two fundamental parameters: control-asymmetry and connectedness. I evaluate this model by meeting two challenges to word meaning discreteness (1) cases where the same pronunciation is associated with multiple senses that are nonetheless interdependent, e.g., English ?smoke,? and (2) cases where the same pronunciation is associated with a family of meanings, minimally distinct from each other organized as a ?cline,? e.g., English ?have.? These cases are not marginal?they are ubiquitous in languages across the world. Any model that captures them is accounting for the meaning system for language. At the heart of the argumentation is the demonstration of how the parameterized space naturally organizes these kinds of cases without appeal for further categorization or segmentation of any kind. From this, I conclude that discreteness in word meaning is epiphenomenal: it is the experience of salience produced by contextual constraints. And that this is possible because, by and large, every time that we become consciously aware of the conceptual structure associated with a pronunciation, i.e., its meaning, we do so under real-time processing conditions which are biased toward producing a specific interpretation in reference to a specific situation in the world. Supporting it is a parameterized space that gives rise to lexico-conceptual representations: generalized algebraic structures necessary for the identification, processing, and encoding of an individual's understanding of the world. ---- Christian Clark Ph.D. Student Department of Linguistics The Ohio State University -------------- next part -------------- An HTML attachment was scrubbed... URL: From court.22 at buckeyemail.osu.edu Wed Jan 31 11:47:51 2024 From: court.22 at buckeyemail.osu.edu (Court, Sara) Date: Wed, 31 Jan 2024 16:47:51 +0000 Subject: [CaCL] CaCL Paper - Tomorrow (Thurs 2/1) - Futrell (2023) "An information-theoretic account of availability effects in language production" Message-ID: Hi all, Tomorrow we'll be discussing Futrell (2023) "An information-theoretic account of availability effects in language production". Here's a link to the paper: https://escholarship.org/uc/item/23q9k7pc See you then, Sara -------------- next part -------------- An HTML attachment was scrubbed... URL: