Large language models (LLMs) enable state-of-the-art semantic capabilities to
be added to software systems such as semantic search of unstructured documents
and text generation. However, these models are computationally expensive. At
scale, the cost of serving thousands of users increases massively affecting
also user experience. To address this problem, semantic caches are used to
check for answers to similar queries (that may have been phrased differently)
without hitting the LLM service. Due to the nature of these semantic cache
techniques that rely on query embeddings, there is a high chance of errors
impacting user confidence in the system. Adopting semantic cache techniques
usually requires testing the effectiveness of a semantic cache (accurate cache
hits and misses) which requires a labelled test set of similar queries and
responses which is often unavailable. In this paper, we present VaryGen, an
approach for using LLMs for test input generation that produces similar
questions from unstructured text documents. Our novel approach uses the
reasoning capabilities of LLMs to 1) adapt queries to the domain, 2) synthesise
subtle variations to queries, and 3) evaluate the synthesised test dataset. We
evaluated our approach in the domain of a student question and answer system by
qualitatively analysing 100 generated queries and result pairs, and conducting
an empirical case study with an open source semantic cache. Our results show
that query pairs satisfy human expectations of similarity and our generated
data demonstrates failure cases of a semantic cache. Additionally, we also
evaluate our approach on Qasper dataset. This work is an important first step
into test input generation for semantic applications and presents
considerations for practitioners when calibrating a semantic cache.

LLMs 被用于测试输入生成以及语义应用，并探讨了语义缓存技术的相关问题和考虑事项。

用于语义缓存测试输入生成的 LLM

LLMs for Test Input Generation for Semantic Caches

Deep Learning (DL) has enabled a paradigm shift in wireless communication
system with data driven end-to-end (E2E) learning and optimization of the
Physical Layer (PHY). By leveraging the representation learning of DL, E2E
systems exhibit enhanced adaptability and performance in complex wireless
environments, fulfilling the demands of 5G and beyond network systems and
applications. The evolution of data-driven techniques in the PHY has enabled
advanced semantic applications across various modalities including text, image,
audio, video, and multi-modal transmissions. These applications transcend from
traditional bit-level communication to semantic-level intelligent communication
systems, which are capable of understanding and adapting to the context and
intent of the data transmission. Although PHY as a DL architecture for
data-driven E2E communication is a key factor in enabling semantic
communication systems (SemCom), and various studies in recent years have
surveyed them separately, their combination has not been thoroughly reviewed.
Additionally, these are emerging fields that are still in their infancy, with
several techniques having been developed and evolved in recent years.
Therefore, this article provides a holistic review of data-driven PHY for E2E
communication system, and their enabling semantic applications across different
modalities. Furthermore, it identifies critical challenges and prospective
research directions, providing a pivotal reference for future development of DL
in PHY and SemCom.

通过对数据驱动的端到端学习和优化的物理层的深度学习表示学习的利用，端到端系统在复杂的无线环境中显现出增强的适应性和性能，满足 5G 及以后的网络系统和应用需求。本文综述数据驱动的物理层在端到端通信系统中的关键作用以及其在不同模态中实现的语义应用，同时指出了关键挑战和未来研究方向，为深度学习在物理层和语义通信中的未来发展提供重要参考。

物理层深度学习：数据驱动端到端通信系统及其支持的语义应用综述

Deep Learning in Physical Layer: Review on Data Driven End-to-End  Communication Systems and their Enabling Semantic Applications

We introduce Graphene, an Open IE system whose goal is to generate accurate,
meaningful and complete propositions that may facilitate a variety of
downstream semantic applications. For this purpose, we transform syntactically
complex input sentences into clean, compact structures in the form of core
facts and accompanying contexts, while identifying the rhetorical relations
that hold between them in order to maintain their semantic relationship. In
that way, we preserve the context of the relational tuples extracted from a
source sentence, generating a novel lightweight semantic representation for
Open IE that enhances the expressiveness of the extracted propositions.

Graphene 是一种基于 Open IE 系统的轻量级的语义表述，能够生成准确、有意义和完整的命题，并维护其上下文和各种语言关系，从而提高所提取命题的表现力。