37,371 research outputs found
Meta-Reasoning: Semantics-Symbol Deconstruction For Large Language Models
Neural-symbolic methods have shown their effectiveness in enhancing the
reasoning abilities of large language models (LLMs). However, existing methods
primarily rely on mapping natural languages to more syntactically complete
formal languages (e.g., Python and SQL). Those approaches necessitate that
reasoning tasks be convertible into programs, which cater more to the computer
execution mindset and deviate from human reasoning habits. To expand the
real-world applicability and flexibility of symbolic methods, we propose
Meta-Reasoning from the scope of linguistics itself. This method empowers LLMs
to deconstruct questions and effectively capture more generalized knowledge
autonomously. We find that Meta-Reasoning achieves improved in-context learning
efficiency, reasoning accuracy, and output stability in six arithmetic and
symbolic reasoning tasks. In particular, when applied to symbolic reasoning
tasks such as Tracking Shuffled Objects, GPT-3 (text-davinci-002) surpasses the
few-shot Chain-of-Thought prompting approach (+37.7%), with 99% accuracy after
a single demonstration of Meta-Reasoning.Comment: Work in progres
Weakly Supervised Reasoning by Neuro-Symbolic Approaches
Deep learning has largely improved the performance of various natural
language processing (NLP) tasks. However, most deep learning models are
black-box machinery, and lack explicit interpretation. In this chapter, we will
introduce our recent progress on neuro-symbolic approaches to NLP, which
combines different schools of AI, namely, symbolism and connectionism.
Generally, we will design a neural system with symbolic latent structures for
an NLP task, and apply reinforcement learning or its relaxation to perform
weakly supervised reasoning in the downstream task. Our framework has been
successfully applied to various tasks, including table query reasoning,
syntactic structure reasoning, information extraction reasoning, and rule
reasoning. For each application, we will introduce the background, our
approach, and experimental results.Comment: Compendium of Neurosymbolic Artificial Intelligence, 665--692, 2023,
IOS Pres
Knowledge Representation, Reasoning and Learning for Non-Extractive Reading Comprehension
abstract: While in recent years deep learning (DL) based approaches have been the popular approach in developing end-to-end question answering (QA) systems, such systems lack several desired properties, such as the ability to do sophisticated reasoning with knowledge, the ability to learn using less resources and interpretability. In this thesis, I explore solutions that aim to address these drawbacks.
Towards this goal, I work with a specific family of reading comprehension tasks, normally referred to as the Non-Extractive Reading Comprehension (NRC), where the given passage does not contain enough information and to correctly answer sophisticated reasoning and ``additional knowledge" is required. I have organized the NRC tasks into three categories. Here I present my solutions to the first two categories and some preliminary results on the third category.
Category 1 NRC tasks refer to the scenarios where the required ``additional knowledge" is missing but there exists a decent natural language parser. For these tasks, I learn the missing ``additional knowledge" with the help of the parser and a novel inductive logic programming. The learned knowledge is then used to answer new questions. Experiments on three NRC tasks show that this approach along with providing an interpretable solution achieves better or comparable accuracy to that of the state-of-the-art DL based approaches.
The category 2 NRC tasks refer to the alternate scenario where the ``additional knowledge" is available but no natural language parser works well for the sentences of the target domain. To deal with these tasks, I present a novel hybrid reasoning approach which combines symbolic and natural language inference (neural reasoning) and ultimately allows symbolic modules to reason over raw text without requiring any translation. Experiments on two NRC tasks shows its effectiveness.
The category 3 neither provide the ``missing knowledge" and nor a good parser. This thesis does not provide an interpretable solution for this category but some preliminary results and analysis of a pure DL based approach. Nonetheless, the thesis shows beyond the world of pure DL based approaches, there are tools that can offer interpretable solutions for challenging tasks without using much resource and possibly with better accuracy.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Neural-symbolic computing has now become the subject of interest of both
academic and industry research laboratories. Graph Neural Networks (GNN) have
been widely used in relational and symbolic domains, with widespread
application of GNNs in combinatorial optimization, constraint satisfaction,
relational reasoning and other scientific domains. The need for improved
explainability, interpretability and trust of AI systems in general demands
principled methodologies, as suggested by neural-symbolic computing. In this
paper, we review the state-of-the-art on the use of GNNs as a model of
neural-symbolic computing. This includes the application of GNNs in several
domains as well as its relationship to current developments in neural-symbolic
computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape
Connectionist natural language parsing
The key developments of two decades of connectionist parsing are reviewed. Connectionist parsers are assessed according to their ability to learn to represent syntactic structures from examples automatically, without being presented with symbolic grammar rules. This review also considers the extent to which connectionist parsers offer computational models of human sentence processing and provide plausible accounts of psycholinguistic data. In considering these issues, special attention is paid to the level of realism, the nature of the modularity, and the type of processing that is to be found in a wide range of parsers
RNNs Implicitly Implement Tensor Product Representations
Recurrent neural networks (RNNs) can learn continuous vector representations
of symbolic structures such as sequences and sentences; these representations
often exhibit linear regularities (analogies). Such regularities motivate our
hypothesis that RNNs that show such regularities implicitly compile symbolic
structures into tensor product representations (TPRs; Smolensky, 1990), which
additively combine tensor products of vectors representing roles (e.g.,
sequence positions) and vectors representing fillers (e.g., particular words).
To test this hypothesis, we introduce Tensor Product Decomposition Networks
(TPDNs), which use TPRs to approximate existing vector representations. We
demonstrate using synthetic data that TPDNs can successfully approximate linear
and tree-based RNN autoencoder representations, suggesting that these
representations exhibit interpretable compositional structure; we explore the
settings that lead RNNs to induce such structure-sensitive representations. By
contrast, further TPDN experiments show that the representations of four models
trained to encode naturally-occurring sentences can be largely approximated
with a bag of words, with only marginal improvements from more sophisticated
structures. We conclude that TPDNs provide a powerful method for interpreting
vector representations, and that standard RNNs can induce compositional
sequence representations that are remarkably well approximated by TPRs; at the
same time, existing training tasks for sentence representation learning may not
be sufficient for inducing robust structural representations.Comment: Accepted to ICLR 201
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