41 research outputs found
ANPL: Compiling Natural Programs with Interactive Decomposition
The advents of Large Language Models (LLMs) have shown promise in augmenting
programming using natural interactions. However, while LLMs are proficient in
compiling common usage patterns into a programming language, e.g., Python, it
remains a challenge how to edit and debug an LLM-generated program. We
introduce ANPL, a programming system that allows users to decompose
user-specific tasks. In an ANPL program, a user can directly manipulate sketch,
which specifies the data flow of the generated program. The user annotates the
modules, or hole with natural language descriptions offloading the expensive
task of generating functionalities to the LLM. Given an ANPL program, the ANPL
compiler generates a cohesive Python program that implements the
functionalities in hole, while respecting the dataflows specified in sketch. We
deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique
tasks that are challenging for state-of-the-art AI systems, showing it
outperforms baseline programming systems that (a) without the ability to
decompose tasks interactively and (b) without the guarantee that the modules
can be correctly composed together. We obtain a dataset consisting of 300/400
ARC tasks that were successfully decomposed and grounded in Python, providing
valuable insights into how humans decompose programmatic tasks. See the dataset
at https://iprc-dip.github.io/DARC
Robust passivity and passification of stochastic fuzzy time-delay systems
The official published version can be obtained from the link below.In this paper, the passivity and passification problems are investigated for a class of uncertain stochastic fuzzy systems with time-varying delays. The fuzzy system is based on the TakagiāSugeno (TāS) model that is often used to represent the complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning. To reflect more realistic dynamical behaviors of the system, both the parameter uncertainties and the stochastic disturbances are considered, where the parameter uncertainties enter into all the system matrices and the stochastic disturbances are given in the form of a Brownian motion. We first propose the definition of robust passivity in the sense of expectation. Then, by utilizing the Lyapunov functional method, the ItĆ“ differential rule and the matrix analysis techniques, we establish several sufficient criteria such that, for all admissible parameter uncertainties and stochastic disturbances, the closed-loop stochastic fuzzy time-delay system is robustly passive in the sense of expectation. The derived criteria, which are either delay-independent or delay-dependent, are expressed in terms of linear matrix inequalities (LMIs) that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.This work was supported by the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the Specialized Research Fund for the Doctoral Program of Higher Education for New Teachers 200802861044, the National Natural Science Foundation of China under Grant 60804028 and the Royal Society of the United Kingdom
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Large language models (LLMs) show their powerful automatic reasoning and
planning capability with a wealth of semantic knowledge about the human world.
However, the grounding problem still hinders the applications of LLMs in the
real-world environment. Existing studies try to fine-tune the LLM or utilize
pre-defined behavior APIs to bridge the LLMs and the environment, which not
only costs huge human efforts to customize for every single task but also
weakens the generality strengths of LLMs. To autonomously ground the LLM onto
the environment, we proposed the Self-Driven Grounding (SDG) framework to
automatically and progressively ground the LLM with self-driven skill learning.
SDG first employs the LLM to propose the hypothesis of sub-goals to achieve
tasks and then verify the feasibility of the hypothesis via interacting with
the underlying environment. Once verified, SDG can then learn generalized
skills with the guidance of these successfully grounded subgoals. These skills
can be further utilized to accomplish more complex tasks which fail to pass the
verification phase. Verified in the famous instruction following task
set-BabyAI, SDG achieves comparable performance in the most challenging tasks
compared with imitation learning methods that cost millions of demonstrations,
proving the effectiveness of learned skills and showing the feasibility and
efficiency of our framework
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
In the field of multi-task reinforcement learning, the modular principle,
which involves specializing functionalities into different modules and
combining them appropriately, has been widely adopted as a promising approach
to prevent the negative transfer problem that performance degradation due to
conflicts between tasks. However, most of the existing multi-task RL methods
only combine shared modules at the task level, ignoring that there may be
conflicts within the task. In addition, these methods do not take into account
that without constraints, some modules may learn similar functions, resulting
in restricting the model's expressiveness and generalization capability of
modular methods. In this paper, we propose the Contrastive Modules with
Temporal Attention(CMTA) method to address these limitations. CMTA constrains
the modules to be different from each other by contrastive learning and
combining shared modules at a finer granularity than the task level with
temporal attention, alleviating the negative transfer within the task and
improving the generalization ability and the performance for multi-task RL. We
conducted the experiment on Meta-World, a multi-task RL benchmark containing
various robotics manipulation tasks. Experimental results show that CMTA
outperforms learning each task individually for the first time and achieves
substantial performance improvements over the baselines.Comment: This paper has been accepted at NeurIPS 2023 as a poste
Online Prototype Alignment for Few-shot Policy Transfer
Domain adaptation in reinforcement learning (RL) mainly deals with the
changes of observation when transferring the policy to a new environment. Many
traditional approaches of domain adaptation in RL manage to learn a mapping
function between the source and target domain in explicit or implicit ways.
However, they typically require access to abundant data from the target domain.
Besides, they often rely on visual clues to learn the mapping function and may
fail when the source domain looks quite different from the target domain. To
address these problems, we propose a novel framework Online Prototype Alignment
(OPA) to learn the mapping function based on the functional similarity of
elements and is able to achieve the few-shot policy transfer within only
several episodes. The key insight of OPA is to introduce an exploration
mechanism that can interact with the unseen elements of the target domain in an
efficient and purposeful manner, and then connect them with the seen elements
in the source domain according to their functionalities (instead of visual
clues). Experimental results show that when the target domain looks visually
different from the source domain, OPA can achieve better transfer performance
even with much fewer samples from the target domain, outperforming prior
methods.Comment: This paper has been accepted at ICML202
Assessing and Understanding Creativity in Large Language Models
In the field of natural language processing, the rapid development of large
language model (LLM) has attracted more and more attention. LLMs have shown a
high level of creativity in various tasks, but the methods for assessing such
creativity are inadequate. The assessment of LLM creativity needs to consider
differences from humans, requiring multi-dimensional measurement while
balancing accuracy and efficiency. This paper aims to establish an efficient
framework for assessing the level of creativity in LLMs. By adapting the
modified Torrance Tests of Creative Thinking, the research evaluates the
creative performance of various LLMs across 7 tasks, emphasizing 4 criteria
including Fluency, Flexibility, Originality, and Elaboration. In this context,
we develop a comprehensive dataset of 700 questions for testing and an
LLM-based evaluation method. In addition, this study presents a novel analysis
of LLMs' responses to diverse prompts and role-play situations. We found that
the creativity of LLMs primarily falls short in originality, while excelling in
elaboration. Besides, the use of prompts and the role-play settings of the
model significantly influence creativity. Additionally, the experimental
results also indicate that collaboration among multiple LLMs can enhance
originality. Notably, our findings reveal a consensus between human evaluations
and LLMs regarding the personality traits that influence creativity. The
findings underscore the significant impact of LLM design on creativity and
bridges artificial intelligence and human creativity, offering insights into
LLMs' creativity and potential applications
Threshold of vapourāpressure deficit constraint on light use efficiency varied with soil water content
Understanding the constraints on light-use efficiency (LUE) induced by high evaporative water demand (vapourāpressure deficit; VPD) and soil water stress (soil moisture content; SMC) is crucial for understanding and simulating vegetation productivity, particularly in the arid and semi-arid regions. However, the relative impacts of VPD and SMC on LUE are unclear, as we lack a mechanistic understanding of impacts and their interactions. In this study, we quantified the relative roles of VPD and SMC in limiting LUE and analysed the interactions among VPD, SMC and LUE using data from CO2 and water flux stations and weather stations along a climatic gradient in the Heihe River Basin, China. We found a threshold of VPD constraint on LUE; above the threshold, LUE decreased at only 3.6% to 23.1% of the rate below the threshold. As SMC decreased, however, the VPD threshold increased, and the reduction of LUE caused by VPD decreased significantly, which is more than half of that in moister regions. Therefore, both VPD and SMC played essential roles in LUE limitation caused by water stress. A threshold also existed for heat flux and the correlation between SMC and LUE; the strength of the correlation first decreased and then increased with increasing VPD. Our results clarified the relative impacts of VPD and SMC on LUE, and can improve simulation and prediction of plant productivity
Generating Natural Cities Using 3D Road Network to Explore Living Structure: A Case Study in Hong Kong
Compared with administrative cities, natural cities can be generally referred to as the areas generated based on the density of different urban facilities (e.g., point of interest, road network, etc.). To some extent, natural cities are outperformed in some related urban studies, such as urban living structure analysis. Nevertheless, traditional ways of generating natural cities are mostly limited to the planar space. Modern cities such as Hong Kong are vertical cities with high buildings, 3D road networks and land uses. Therefore, traditional nature cities could be biased when applied to 3D cities. In this work, a 3D road network in Hong Kong is adopted to extract true road intersections and generate modified natural cities to explore urban living structures. Numerous living structure units are classified into two parts: tiny and serried ones representing natural cities and vast ones representing rural areas. The classification method applies head/tail breaks, and a clustering algorithm was fitted for heavy-tailed distribution. According to the living structure theory, the living structures of the proposed natural cities and traditional natural cities based on the same road network in Hong Kong are compared. The findings show that the distribution of modified natural city regions is more reasonable compared with typical ones. The improved model will more clearly show the inherent living structure of the city and will allow an analysis of the relationship between the part and wholeness of the city