18 research outputs found
Batch Prompting: Efficient Inference with Large Language Model APIs
Performing inference on hundreds of thousands of samples with large language
models (LLMs) can be computationally and financially costly. We propose batch
prompting, a simple alternative prompting approach that enables the LLM to run
inference in batches, instead of one sample at a time. Our method reduces both
token and time costs while retaining downstream performance. We theoretically
demonstrate that under a few-shot in-context learning setting, the inference
costs decrease almost inverse linearly with the number of samples in each
batch. We extensively validate the effectiveness of batch prompting on ten
datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch
prompting significantly~(up to with six samples in batch) reduces the
LLM (Codex) inference token and time costs while achieving better or comparable
performance. Our analysis shows that the number of samples in each batch and
the complexity of tasks affect its performance. Further, batch prompting can be
applied across different LLMs and reasoning methods.Comment: 18 pages, 9 figure
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models
optimized for both natural language and coding capabilities to serve as the
backbone of versatile language agents. The evolution from language chat models
to functional language agents demands that models not only master human
interaction, reasoning, and planning but also ensure grounding in the relevant
environments. This calls for a harmonious blend of language and coding
capabilities in the models. Lemur and Lemur-Chat are proposed to address this
necessity, demonstrating balanced proficiencies in both domains, unlike
existing open-source models that tend to specialize in either. Through
meticulous pre-training using a code-intensive corpus and instruction
fine-tuning on text and code data, our models achieve state-of-the-art averaged
performance across diverse text and coding benchmarks among open-source models.
Comprehensive experiments demonstrate Lemur's superiority over existing
open-source models and its proficiency across various agent tasks involving
human communication, tool usage, and interaction under fully- and partially-
observable environments. The harmonization between natural and programming
languages enables Lemur-Chat to significantly narrow the gap with proprietary
models on agent abilities, providing key insights into developing advanced
open-source agents adept at reasoning, planning, and operating seamlessly
across environments. https://github.com/OpenLemur/Lemu
Validation of the plasma-wall self-organization model for density limit in ECRH-assisted start-up of Ohmic discharges on J-TEXT
A recently developed plasma-wall self-organization (PWSO) model predicts a
significantly enhanced density limit, which may be attainable in tokamaks with
ECRH-assisted ohmic startup and sufficiently high initial neutral density.
Experiments have been conducted on J-TEXT to validate such a density limit
scenario based on this model. Experimental results demonstrate that increasing
the pre-filled gas pressure or ECRH power during the startup phase can
effectively enhance plasma purity and raise the density limit at the flat-top.
Despite the dominant carbon fraction in the wall material, some discharges
approach the edge of the density-free regime of the 1D model of PWSO.Comment: 17 pages, 8 figure
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART,
have been well applied to table question answering by UNIFIEDSKG and TAPEX,
respectively, and demonstrated state-of-the-art results on multiple benchmarks.
However, auto-regressive PLMs are challenged by recent emerging numerical
reasoning datasets, such as TAT-QA, due to the error-prone implicit
calculation. In this paper, we present TaCube, to pre-compute
aggregation/arithmetic results for the table in advance, so that they are handy
and readily available for PLMs to answer numerical reasoning questions. TaCube
systematically and comprehensively covers a collection of computational
operations over table segments. By simply concatenating TaCube to the input
sequence of PLMs, it shows significant experimental effectiveness. TaCube
promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new
state-of-the-art results on WikiTQ (59.6% denotation accuracy). TaCube's
improvements on numerical reasoning cases are even more notable: on TAT-QA,
TaCube promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5%
on average, 36.6% on substraction, and 22.2% on division. We believe that
TaCube is a general and portable pre-computation solution that can be
potentially integrated to various numerical reasoning framework
Effect of Process Parameters on Tensile Mechanical Properties of 3D Printing Continuous Carbon Fiber-Reinforced PLA Composites
Three-dimensional (3D) printing continuous carbon fiber-reinforced polylactic acid (PLA) composites offer excellent tensile mechanical properties. The present study aimed to research the effect of process parameters on the tensile mechanical properties of 3D printing composite specimens through a series of mechanical experiments. The main printing parameters, including layer height, extrusion width, printing temperature, and printing speed are changed to manufacture specimens based on the modified fused filament fabrication 3D printer, and the tensile mechanical properties of 3D printing continuous carbon fiber-reinforced PLA composites are presented. By comparing the outcomes of experiments, the results show that relative fiber content has a significant impact on mechanical properties and the ratio of carbon fibers in composites is influenced by layer height and extrusion width. The tensile mechanical properties of continuous carbon fiber-reinforced composites gradually decrease with an increase of layer height and extrusion width. In addition, printing temperature and speed also affect the fiber matrix interface, i.e., tensile mechanical properties increase as the printing temperature rises, while the tensile mechanical properties decrease when the printing speed increases. Furthermore, the strengthening mechanism on the tensile mechanical properties is that external loads subjected to the components can be transferred to the carbon fibers through the fiber-matrix interface. Additionally, SEM images suggest that the main weakness of continuous carbon fiber-reinforced 3D printing composites exists in the fiber-matrix interface, and the main failure is the pull-out of the fiber caused by the interface destruction