A Computationally Grounded Framework for Cognitive Attitudes (extended version)
- URL: http://arxiv.org/abs/2412.14073v1
- Date: Wed, 18 Dec 2024 17:17:07 GMT
- Title: A Computationally Grounded Framework for Cognitive Attitudes (extended version)
- Authors: Tiago de Lima, Emiliano Lorini, Elise Perrotin, François Schwarzentruber,
- Abstract summary: We introduce a novel language for reasoning about agents' cognitive attitudes of both epistemic and motivational type.
Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, realistic attraction and realistic repulsion.
We present a dynamic extension of the language that supports reasoning about the effects of belief change operations.
- Score: 14.866324473006255
- License:
- Abstract: We introduce a novel language for reasoning about agents' cognitive attitudes of both epistemic and motivational type. We interpret it by means of a computationally grounded semantics using belief bases. Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, realistic attraction and realistic repulsion. We give an axiomatization and show that our operators are not mutually expressible and that they can be combined to represent a large variety of psychological concepts including ambivalence, indifference, being motivated, being demotivated and preference. We present a dynamic extension of the language that supports reasoning about the effects of belief change operations. Finally, we provide a succinct formulation of model checking for our languages and a PSPACE model checking algorithm relying on a reduction into TQBF. We present some experimental results for the implemented algorithm on computation time in a concrete example.
Related papers
- Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning [31.632816425798108]
Tokenization is a necessary component within the current architecture of many language models.
We show that tokenization pretraining can be a backdoor for bias and other unwanted content.
We relay evidence that the tokenization algorithm's objective function impacts the large language model's cognition.
arXiv Detail & Related papers (2024-12-14T18:18:52Z) - Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.
The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation.
We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
arXiv Detail & Related papers (2023-11-08T20:41:18Z) - From Heuristic to Analytic: Cognitively Motivated Strategies for
Coherent Physical Commonsense Reasoning [66.98861219674039]
Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions.
Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
arXiv Detail & Related papers (2023-10-24T19:46:04Z) - Generative Models as a Complex Systems Science: How can we make sense of
large language model behavior? [75.79305790453654]
Coaxing out desired behavior from pretrained models, while avoiding undesirable ones, has redefined NLP.
We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance.
arXiv Detail & Related papers (2023-07-31T22:58:41Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play
Multi-Character Belief Tracker [72.09076317574238]
ToM is a plug-and-play approach to investigate the belief states of characters in reading comprehension.
We show that ToM enhances off-the-shelf neural network theory mind in a zero-order setting while showing robust out-of-distribution performance compared to supervised baselines.
arXiv Detail & Related papers (2023-06-01T17:24:35Z) - ReAct: Synergizing Reasoning and Acting in Language Models [44.746116256516046]
We show that large language models (LLMs) can generate both reasoning traces and task-specific actions in an interleaved manner.
We apply our approach, named ReAct, to a diverse set of language and decision making tasks.
ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API.
arXiv Detail & Related papers (2022-10-06T01:00:32Z) - Pretraining on Interactions for Learning Grounded Affordance
Representations [22.290431852705662]
We train a neural network to predict objects' trajectories in a simulated interaction.
We show that our network's latent representations differentiate between both observed and unobserved affordances.
Our results suggest a way in which modern deep learning approaches to grounded language learning can be integrated with traditional formal semantic notions of lexical representations.
arXiv Detail & Related papers (2022-07-05T19:19:53Z) - Provable Limitations of Acquiring Meaning from Ungrounded Form: What
will Future Language Models Understand? [87.20342701232869]
We investigate the abilities of ungrounded systems to acquire meaning.
We study whether assertions enable a system to emulate representations preserving semantic relations like equivalence.
We find that assertions enable semantic emulation if all expressions in the language are referentially transparent.
However, if the language uses non-transparent patterns like variable binding, we show that emulation can become an uncomputable problem.
arXiv Detail & Related papers (2021-04-22T01:00:17Z) - Probing Neural Language Models for Human Tacit Assumptions [36.63841251126978]
Humans carry stereotypic tacit assumptions (STAs) or propositional beliefs about generic concepts.
We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs.
arXiv Detail & Related papers (2020-04-10T01:48:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.