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Found 22 papers, 16 papers with code
Learning a Hierarchical Planner from Humans in Multiple Generations
Compared to learning from demonstrations or experiences, programmatic learning allows the machine to acquire a novel skill as soon as the program is written, and, by building a library of programs, a machine can quickly learn how to perform complex tasks.
Causal interventions expose implicit situation models for commonsense language understanding
Accounts of human language processing have long appealed to implicit ``situation models'' that enrich comprehension with relevant but unstated world knowledge.
Overinformative Question Answering by Humans and Machines
When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no".
Semantic uncertainty guides the extension of conventions to new referents
A long tradition of studies in psycholinguistics has examined the formation and generalization of ad hoc conventions in reference games, showing how newly acquired conventions for a given target transfer to new referential contexts.
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines.
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
Identifying concept libraries from language about object structure
Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations.
Linguistic communication as (inverse) reward design
We then define a pragmatic listener which performs inverse reward design by jointly inferring the speaker's latent horizon and rewards.
Probing BERT's priors with serial reproduction chains
Sampling is a promising bottom-up method for exposing what generative models have learned about language, but it remains unclear how to generate representative samples from popular masked language models (MLMs) like BERT.
A pragmatic account of the weak evidence effect
Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a pragmatic account for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect.
Visual resemblance and communicative context constrain the emergence of graphical conventions
From photorealistic sketches to schematic diagrams, drawing provides a versatile medium for communicating about the visual world.
Learning to communicate about shared procedural abstractions
Many real-world tasks require agents to coordinate their behavior to achieve shared goals.
Extending rational models of communication from beliefs to actions
Speakers communicate to influence their partner's beliefs and shape their actions.
Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task
There is substantial variability in the expectations that communication partners bring into interactions, creating the potential for misunderstandings.
From partners to populations: A hierarchical Bayesian account of coordination and convention
Languages are powerful solutions to coordination problems: they provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads.
Investigating representations of verb bias in neural language models
Languages typically provide more than one grammatical construction to express certain types of messages.
Learning Rewards from Linguistic Feedback
The sentiment models outperform the inference network, with the "pragmatic" model approaching human performance.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
A key property of linguistic conventions is that they hold over an entire community of speakers, allowing us to communicate efficiently even with people we have never met before.
Characterizing the dynamics of learning in repeated reference games
The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful.
Continual adaptation for efficient machine communication
To communicate with new partners in new contexts, humans rapidly form new linguistic conventions.
When redundancy is useful: A Bayesian approach to 'overinformative' referring expressions
Crucially, we relax the assumption that informativeness is computed with respect to a deterministic Boolean semantics, in favor of a non-deterministic continuous semantics.
The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective
In Experiment 1, we manipulated the presence or absence of occlusions in a director-matcher task and found that speakers spontaneously produced more informative descriptions to account for "known unknowns" in their partner's private view.
