Harvey Lederman: Propositional Attitudes and Reference in Language Models

Harvey Lederman: Propositional Attitudes and Reference in Language Models

Author: Daniel Bashir January 11, 2024 Duration: 2:10:34

In episode 106 of The Gradient Podcast, Daniel Bashir speaks to Professor Harvey Lederman.

Professor Lederman is a professor of philosophy at UT Austin. He has broad interests in contemporary philosophy and in the history of philosophy: his areas of specialty include philosophical logic, the Ming dynasty philosopher Wang Yangming, epistemology, and philosophy of language. He has recently been working on incomplete preferences, on trying in the philosophy of language, and on Wang Yangming’s moral metaphysics.

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Outline:

* (00:00) Intro

* (02:15) Harvey’s background

* (05:30) Higher-order metaphysics and propositional attitudes

* (06:25) Motivations

* (12:25) Setup: syntactic types and ontological categories

* (25:11) What makes higher-order languages meaningful and not vague?

* (25:57) Higher-order languages corresponding to the world

* (30:52) Extreme vagueness

* (35:32) Desirable features of languages and important questions in philosophy

* (36:42) Higher-order identity

* (40:32) Intuitions about mental content, language, context-sensitivity

* (50:42) Perspectivism

* (51:32) Co-referring names, identity statements

* (55:42) The paper’s approach, “know” as context-sensitive

* (57:24) Propositional attitude psychology and mentalese generalizations

* (59:57) The “good standing” of theorizing about propositional attitudes

* (1:02:22) Mentalese

* (1:03:32) “Does knowledge imply belief?” — when a question does not have good standing

* (1:06:17) Sense, Reference, and Substitution

* (1:07:07) Fregeans and the principle of Substitution

* (1:12:12) Follow-up work to this paper

* (1:13:39) Do Language Models Produce Reference Like Libraries or Like Librarians?

* (1:15:02) Bibliotechnism

* (1:19:08) Inscriptions and reference, what it takes for something to refer

* (1:22:37) Derivative and basic reference

* (1:24:47) Intuition: n-gram models and reference

* (1:28:22) Meaningfulness in sentences produced by n-gram models

* (1:30:40) Bibliotechnism and LLMs, disanalogies to n-grams

* (1:33:17) On other recent work (vector grounding, do LMs refer?, etc.)

* (1:40:12) Causal connections and reference, how bibliotechnism makes good on the meanings of sentences

* (1:45:46) RLHF, sensitivity to truth and meaningfulness

* (1:48:47) Intelligibility

* (1:50:52) When LLMs produce novel reference

* (1:53:37) Novel reference vs. find-replace

* (1:56:00) Directionality example

* (1:58:22) Human intentions and derivative reference

* (2:00:47) Between bibliotechnism and agency

* (2:05:32) Where do invented names / novel reference come from?

* (2:07:17) Further questions

* (2:10:04) Outro

Links:

* Harvey’s homepage and Twitter

* Papers discussed

* Higher-order metaphysics and propositional attitudes

* Perspectivism

* Sense, Reference, and Substitution

* Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs



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Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
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