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Found 11 papers, 5 papers with code
Understanding Epistemic Language with a Bayesian Theory of Mind
How do people understand and evaluate claims about others' beliefs, even though these beliefs cannot be directly observed?
People use fast, goal-directed simulation to reason about novel games
People can evaluate features of problems and their potential solutions well before we can effectively solve them.
Finding structure in logographic writing with library learning
Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency.
Grounding Language about Belief in a Bayesian Theory-of-Mind
In this paper, we take a step towards an answer by grounding the semantics of belief statements in a Bayesian theory-of-mind: By modeling how humans jointly infer coherent sets of goals, beliefs, and plans that explain an agent's actions, then evaluating statements about the agent's beliefs against these inferences via epistemic logic, our framework provides a conceptual role semantics for belief, explaining the gradedness and compositionality of human belief attributions, as well as their intimate connection with goals and plans.
Learning adaptive planning representations with natural language guidance
Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand.
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs.
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
To test our model, we design and run a human experiment on a linguistic goal inference task.
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Our architecture integrates two computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for commonsense reasoning; and we model meaning construction with large language models (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language.
Evaluating Language Models for Mathematics through Interactions
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants.
Evaluating statistical language models as pragmatic reasoners
These results inform the inferential capacity of statistical language models, and their use in pragmatic and semantic parsing applications.
Top-Down Synthesis for Library Learning
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL).
