77,652 research outputs found
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
We introduce a new benchmark, LLF-Bench (Learning from Language Feedback
Benchmark; pronounced as "elf-bench"), to evaluate the ability of AI agents to
interactively learn from natural language feedback and instructions. Learning
from language feedback (LLF) is essential for people, largely because the rich
information this feedback provides can help a learner avoid much of trial and
error and thereby speed up the learning process. Large Language Models (LLMs)
have recently enabled AI agents to comprehend natural language -- and hence AI
agents can potentially benefit from language feedback during learning like
humans do. But existing interactive benchmarks do not assess this crucial
capability: they either use numeric reward feedback or require no learning at
all (only planning or information retrieval). LLF-Bench is designed to fill
this omission. LLF-Bench is a diverse collection of sequential decision-making
tasks that includes user recommendation, poem writing, navigation, and robot
control. The objective of an agent is to interactively solve these tasks based
on their natural-language instructions and the feedback received after taking
actions. Crucially, to ensure that the agent actually "learns" from the
feedback, LLF-Bench implements several randomization techniques (such as
paraphrasing and environment randomization) to ensure that the task isn't
familiar to the agent and that the agent is robust to various verbalizations.
In addition, LLF-Bench provides a unified OpenAI Gym interface for all its
tasks and allows the users to easily configure the information the feedback
conveys (among suggestion, explanation, and instantaneous performance) to study
how agents respond to different types of feedback. Together, these features
make LLF-Bench a unique research platform for developing and testing LLF
agents
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A corpus-based analysis of route instructions in human-robot interaction
This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was
operated by naĂŻve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and
demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of
this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for
endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well
as the environment
A Questioning Agent for Literary Discussion
Developing a compelling and cohesive thesis for analytical writing can be a daunting task, even for those who have produced many written works, and finding others to engage with in literary discussion can be equally challenging. In this paper, we describe our solution: Questioner, a discussion tool that engages users in conversation about an academic topic of their choosing for the purpose of collecting thoughts on a subject and constructing an argument. This system will ask informed questions that prompt further discussion about the topic and provide a discussion report after the conversation has ended. We found that our system is effective in providing users with unique questions and excerpts that are relevant, significant, and engaging. Such a discussion tool can be used by writers building theses, students looking for study tools, and instructors who want to create individualized in-class discussions. Once more data is gathered, efficient and accurate machine learning models can be used to further improve the quality of question and excerpt recommendations. Co-creative discussion tools like Questioner are useful in assisting users in developing critical analyses of written works, helping to maximize human creativity
On-Disk Data Processing: Issues and Future Directions
In this paper, we present a survey of "on-disk" data processing (ODDP). ODDP,
which is a form of near-data processing, refers to the computing arrangement
where the secondary storage drives have the data processing capability.
Proposed ODDP schemes vary widely in terms of the data processing capability,
target applications, architecture and the kind of storage drive employed. Some
ODDP schemes provide only a specific but heavily used operation like sort
whereas some provide a full range of operations. Recently, with the advent of
Solid State Drives, powerful and extensive ODDP solutions have been proposed.
In this paper, we present a thorough review of architectures developed for
different on-disk processing approaches along with current and future
challenges and also identify the future directions which ODDP can take.Comment: 24 pages, 17 Figures, 3 Table
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