Related papers: Learning to refer informatively by amortizing pragmatic reasoning

Learning to refer informatively by amortizing pragmatic reasoning

URL: http://arxiv.org/abs/2006.00418v1
Date: Sun, 31 May 2020 02:52:22 GMT
Title: Learning to refer informatively by amortizing pragmatic reasoning
Authors: Julia White, Jesse Mu, Noah D. Goodman
Abstract summary: We explore the idea that speakers might learn to amortize the cost of Rational Speech Acts over time. We find that our amortized model is able to quickly generate language that is effective and concise across a range of contexts.
Score: 35.71540493379324
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016). However, RSA represents ideal reasoning in an unconstrained setting. We explore the idea that speakers might learn to amortize the cost of RSA computation over time by directly optimizing for successful communication with an internal listener model. In simulations with grounded neural speakers and listeners across two communication game datasets representing synthetic and human-generated data, we find that our amortized model is able to quickly generate language that is effective and concise across a range of contexts, without the need for explicit pragmatic reasoning.

Related papers

Are LLMs good pragmatic speakers? [3.4113474745671923]
Large language models (LLMs) are trained on data assumed to include natural language pragmatics, but do they actually behave like pragmatic speakers? We attempt to answer this question using the Rational Speech Act (RSA) framework, which models pragmatic reasoning in human communication. We find that while scores from the LLM have some positive correlation with those from RSA, there isn't sufficient evidence to claim that it behaves like a pragmatic speaker.
arXiv Detail & Related papers (2024-11-03T13:23:18Z)
Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning [72.21876989058858]
We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games. In this model speakers and listeners search for contextually appropriate utterance--meaning mappings that are both close to game-theoretically optimal conventions and close to a shared, ''default'' semantics.
arXiv Detail & Related papers (2023-11-16T09:42:36Z)
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models [34.22393697176282]
We propose the Meta-Reasoning to broaden symbolic methods' applicability and adaptability in the real world. This method empowers LLMs to deconstruct reasoning-independent semantic information into generic symbolic representations. We conduct extensive experiments on more than ten datasets encompassing conventional reasoning tasks like arithmetic, symbolic, and logical reasoning, and the more complex interactive reasoning tasks like theory-of-mind reasoning.
arXiv Detail & Related papers (2023-06-30T17:38:10Z)
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)
DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
arXiv Detail & Related papers (2023-06-15T10:41:23Z)
Evaluating statistical language models as pragmatic reasoners [39.72348730045737]
We evaluate the capacity of large language models to infer meanings of pragmatic utterances. We find that LLMs can derive context-grounded, human-like distributions over the interpretations of several complex pragmatic utterances. Results inform the inferential capacity of statistical language models, and their use in pragmatic and semantic parsing applications.
arXiv Detail & Related papers (2023-05-01T18:22:10Z)
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex [48.588772371355816]
This paper presents the first empirical study on the adversarial robustness of a large prompt-based language model of code, codex. Our results demonstrate that the state-of-the-art (SOTA) code-language models are vulnerable to carefully crafted adversarial examples.
arXiv Detail & Related papers (2023-01-30T13:21:00Z)
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning [73.3035118224719]
We propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA.
arXiv Detail & Related papers (2022-12-19T07:40:02Z)
Scalable pragmatic communication via self-supervision [14.01704261285015]
We propose an architecture and learning process in which agents acquire pragmatic policies via self-supervision instead of imitating human data. This work suggests a new principled approach for equipping artificial agents with pragmatic skills via self-supervision.
arXiv Detail & Related papers (2021-08-12T15:28:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.