29 research outputs found
CFD Analysis of Supersonic Exhaust in a Scramjet Engine
When pressures and temperatures become so high in supersonic flight that it is no longer efficient to slow the oncoming flow to subsonic speeds for combustion, a scramjet (supersonic combustion ramjet) is used in place of a ramjet. This paper isaimed at modeling the supersonic flow inside Scramjet engine using the Computational Fluid Dynamics ANSYS Fluent. The purpose of this test is to validate FLUENT's ability to predict reflecting shock waves
and their effect on wall pressure distribution and heat transfer. Supersonic flow from a nozzle that represents the exhaust nozzle of a supersonic combustion ramjet (SCRAMJET) is modeled. Jet from the nozzle is issued into a domain which is bounded on one side by an afterbody wall whic
h is parallel to the centerline of the nozzle. Shocks propagating from the nozzle exit reflect from the afterbody. Measured values of the dist
ribution of wall pressure and heat transfer rate along the afterbody are used to validate the CFD simulation.In this study, k-ε model has been used to examine supersonic flow in a model scramjet exhaust. The configuration used is similar to the DLR (German Aerospace Center) scramjet model and it is consists of a one-sided divergent channel
with wedge-shaped and without wedge shaped. For the purpose of validation, the k-ε results are compared with experimental data for temperature at the bottom wall. In addition, qualitative comparisons are also made between predicted and measured shadowgraph images. The k-ε computatio
ns are capable of predicting flow simulations well
and good
Prompting with Pseudo-Code Instructions
Prompting with natural language instructions has recently emerged as a
popular method of harnessing the capabilities of large language models. Given
the inherent ambiguity present in natural language, it is intuitive to consider
the possible advantages of prompting with less ambiguous prompt styles, such as
the use of pseudo-code.
In this paper we explore if prompting via pseudo-code instructions helps
improve the performance of pre-trained language models. We manually create a
dataset of pseudo-code prompts for 132 different tasks spanning classification,
QA and generative language tasks, sourced from the Super-NaturalInstructions
dataset. Using these prompts along with their counterparts in natural language,
we study their performance on two LLM families - BLOOM and CodeGen. Our
experiments show that using pseudo-code instructions leads to better results,
with an average increase (absolute) of 7-16 points in F1 scores for
classification tasks and an improvement (relative) of 12-38% in aggregate
ROUGE-L scores across all tasks. We include detailed ablation studies which
indicate that code comments, docstrings, and the structural clues encoded in
pseudo-code all contribute towards the improvement in performance.
To the best of our knowledge our work is the first to demonstrate how
pseudo-code prompts can be helpful in improving the performance of pre-trained
LMs.Comment: Published in EMNLP 2023 main trac
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities
LLMs have demonstrated remarkable capability for understanding semantics, but
they often struggle with understanding pragmatics. To demonstrate this fact, we
release a Pragmatics Understanding Benchmark (PUB) dataset consisting of
fourteen tasks in four pragmatics phenomena, namely, Implicature,
Presupposition, Reference, and Deixis. We curated high-quality test sets for
each task, consisting of Multiple Choice Question Answers (MCQA). PUB includes
a total of 28k data points, 6.1k of which have been created by us, and the rest
are adapted from existing datasets. We evaluated nine models varying in the
number of parameters and type of training. Our study indicates that fine-tuning
for instruction-following and chat significantly enhances the pragmatics
capabilities of smaller language models. However, for larger models, the base
versions perform comparably with their chat-adapted counterparts. Additionally,
there is a noticeable performance gap between human capabilities and model
capabilities. Furthermore, unlike the consistent performance of humans across
various tasks, the models demonstrate variability in their proficiency, with
performance levels fluctuating due to different hints and the complexities of
tasks within the same dataset. Overall, the benchmark aims to provide a
comprehensive evaluation of LLM's ability to handle real-world language tasks
that require pragmatic reasoning
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages
We present, Naamapadam, the largest publicly available Named Entity
Recognition (NER) dataset for the 11 major Indian languages from two language
families. The dataset contains more than 400k sentences annotated with a total
of at least 100k entities from three standard entity categories (Person,
Location, and, Organization) for 9 out of the 11 languages. The training
dataset has been automatically created from the Samanantar parallel corpus by
projecting automatically tagged entities from an English sentence to the
corresponding Indian language translation. We also create manually annotated
testsets for 9 languages. We demonstrate the utility of the obtained dataset on
the Naamapadam-test dataset. We also release IndicNER, a multilingual IndicBERT
model fine-tuned on Naamapadam training set. IndicNER achieves an F1 score of
more than for out of test languages. The dataset and models are
available under open-source licences at
https://ai4bharat.iitm.ac.in/naamapadam.Comment: ACL 202
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for
Hindi. Airavata was created by fine-tuning OpenHathi with diverse,
instruction-tuning Hindi datasets to make it better suited for assistive tasks.
Along with the model, we also share the IndicInstruct dataset, which is a
collection of diverse instruction-tuning datasets to enable further research
for Indic LLMs. Additionally, we present evaluation benchmarks and a framework
for assessing LLM performance across tasks in Hindi. Currently, Airavata
supports Hindi, but we plan to expand this to all 22 scheduled Indic languages.
You can access all artifacts at https://ai4bharat.github.io/airavata.Comment: Work in progres