Tootfinder

NLP Methods for Detecting Novel LLM Jailbreaks and Keyword Analysis with BERT
John Hawkins, Aditya Pramar, Rodney Beard, Rohitash Chandra
https://arxiv.org/abs/2510.01644 https:…

NLP Methods for Detecting Novel LLM Jailbreaks and Keyword Analysis with BERT
Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-called jailbreak prompts are designed to trick the LLM into circumventing the safety guardrails put in place to keep responses acceptable to the developer's policies. In this study, we analyse the ability of different machine learning models to distinguish jailbreak prompts from genuine uses, including looking at our abi…

from my link log —
Unification-free ("keyword") type checking.
https://www.haskellforall.com/2024/02/unification-free-keyword-type-checking.html
saved 2025-09-01

Unification-free ("keyword") type checking
Unification-free ("keyword") type checking From my perspective, one of the biggest open problems in imple...

Use the required Keyword in C# To Enforce Property Initialisation - #dotNet
https://improveandrepeat.com/2025/08/use-the-required-keyword-i…

Use the required Keyword in C# To Enforce Property Initialisation - Improve & Repeat
None

SandCell: Sandboxing Rust Beyond Unsafe Code
Jialun Zhang, Merve G\"ulmez, Thomas Nyman, Gang Tan
https://arxiv.org/abs/2509.24032 https://arxiv.org/p…

SandCell: Sandboxing Rust Beyond Unsafe Code
Rust is a modern systems programming language that ensures memory safety by enforcing ownership and borrowing rules at compile time. While the unsafe keyword allows programmers to bypass these restrictions, it introduces significant risks. Various approaches for isolating unsafe code to protect safe Rust from vulnerabilities have been proposed, yet these methods provide only fixed isolation boundaries and do not accommodate expressive policies that require sandboxing both safe and unsafe code. …

Just Dork and Crawl: Measuring Illegal Online Gambling Defacement in Indonesian Websites
Luqman Muhammad Zagi, Girindro Pringgo Digdo, Wervyan Shalannanda
https://arxiv.org/abs/2508.19368

Just Dork and Crawl: Measuring Illegal Online Gambling Defacement in Indonesian Websites
This study investigates the defacement of Indonesian websites by actors promoting illegal online gambling. Using a lightweight methodology that combines keyword-driven dorking with systematic crawling, we identified 453 defaced webpages within one month. Although dorking alone yielded a false positive rate of approximately 20.3\%, the integration of crawling and keyword-counting enabled reliable differentiation between true and false positives. Our measurements revealed diverse defacement behav…

Bibliometric-enhanced Systematic Literature Review of EEG in Education: Learning Concepts, Computational Methods, and Research Opportunities
Adi Wijaya, Said Hasibuan, Wiga Maulana Baihaqi, Rizki Darmawan, Rifkie Primartha, Catur Supriyanto
https://arxiv.org/abs/2509.26083

Bibliometric-enhanced Systematic Literature Review of EEG in Education: Learning Concepts, Computational Methods, and Research Opportunities
Application of electroencephalography (EEG) in educational research has grown substantially, yet a comprehensive integration of methodological frameworks, educational constructs, computational methods, and research gaps remains limited. This study applies a Bibliometric-enhanced Systematic Literature Review (BenSLR) to provide a systematic overview of EEG in education. Literature was extracted from Scopus, screened, and analyzed, with keyword co-occurrence evaluated using VOSviewer and emerging…

How good are LLMs at Retrieving Documents in a Specific Domain?
Nafis Tanveer Islam, Zhiming Zhao
https://arxiv.org/abs/2509.22658 https://arxiv.org/pdf/25…

How good are LLMs at Retrieving Documents in a Specific Domain?
Classical search engines using indexing methods in data infrastructures primarily allow keyword-based queries to retrieve content. While these indexing-based methods are highly scalable and efficient, due to a lack of an appropriate evaluation dataset and a limited understanding of semantics, they often fail to capture the user's intent and generate incomplete responses during evaluation. This problem also extends to domain-specific search systems that utilize a Knowledge Base (KB) to access da…

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan
https://arxiv.org/abs/2509.14507

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
Natural Language to SQL (NL2SQL) provides a new model-centric paradigm that simplifies database access for non-technical users by converting natural language queries into SQL commands. Recent advancements, particularly those integrating Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning, have made significant strides in enhancing NL2SQL performance. However, challenges such as inaccurate task decomposition and keyword extraction by LLMs remain major bottlenecks, often lea…

Back in 2010 I made the "Twitter Monkey" partly in thanks in to the work of @… for code and ideas.
Maybe we should build a new version that is Fediverse-powered.
https://

Twitter Monkey
Behold... the Arduino Powered Twitter Monkey! The Twitter Monkey is powered by an Arduino and two servo motors. He patiently monitors Twitter for a specified keyword, and when he sees that keyword, he goes bananas, flapping his arms up and down like a maniac. (For his debut at Web414, the keyword is web414.) Twitter Monkey [...]

Zero-Shot KWS for Children's Speech using Layer-Wise Features from SSL Models
Subham Kutum, Abhijit Sinha, Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Mahesh Chandra Govil
https://arxiv.org/abs/2508.21248

Zero-Shot KWS for Children's Speech using Layer-Wise Features from SSL Models
Numerous methods have been proposed to enhance Keyword Spotting (KWS) in adult speech, but children's speech presents unique challenges for KWS systems due to its distinct acoustic and linguistic characteristics. This paper introduces a zero-shot KWS approach that leverages state-of-the-art self-supervised learning (SSL) models, including Wav2Vec2, HuBERT and Data2Vec. Features are extracted layer-wise from these SSL models and used to train a Kaldi-based DNN KWS system. The WSJCAM0 adult speec…

Using the `extension` keyword in #csharp 14 is kind of awkward. It would be nicer to be able to use it as a top-level declaration without the wrapping static class.
Before you say, "where would they exist in that case?”, each namespace would have a catch-all static class that would hold these extension methods if there is no wrapping class.

The init Keyword for Immutable Properties in C# #dotNet
https://improveandrepeat.com/2025/08/the-init-keyword-for-immutable-properties-in-c/

The init Keyword for Immutable Properties in C# - Improve & Repeat
None

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
Hiroki Sayama
https://arxiv.org/abs/2509.11997 https://ar…

Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
The complex systems keyword diagram generated by the author in 2010 has been used widely in a variety of educational and outreach purposes, but it definitely needs a major update and reorganization. This short paper reports our recent attempt to update the keyword diagram using information collected from the following multiple sources: (a) collective feedback posted on social media, (b) recent reference books on complex systems and network science, (c) online resources on complex systems, and (…

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
Wei Huang, De-Tian Chu, Lin-Yuan Bai, Wei Kang, Hai-Tao Zhang, Bo Li, Zhi-Mo Han, Jing Ge, Hai-Feng Lin
https://arxiv.org/abs/2509.21129

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass filters. Traditional spam detection systems, which rely on static rules or single-modality models, struggle to integrate heterogeneous signals or to continuously adapt, leading to rapid performance degra…

Galaxy XR is the first Android XR headset and features Gemini Live that understands your surroundings and lets users interact with apps hands-free (Shahram Izadi/The Keyword)
https://blog.google/products/android/samsung-galaxy-xr/

Introducing Galaxy XR, the first Android XR headset
Galaxy XR is now available today. Here’s what to expect.

Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting
Zhiqi Ai, Han Cheng, Yuxin Wang, Shiyi Mu, Shugong Xu, Yongjin Zhou
https://arxiv.org/abs/2510.10740 https:/…

Dual Data Scaling for Robust Two-Stage User-Defined Keyword Spotting
In this paper, we propose DS-KWS, a two-stage framework for robust user-defined keyword spotting. It combines a CTC-based method with a streaming phoneme search module to locate candidate segments, followed by a QbyT-based method with a phoneme matcher module for verification at both the phoneme and utterance levels. To further improve performance, we introduce a dual data scaling strategy: (1) expanding the ASR corpus from 460 to 1,460 hours to strengthen the acoustic model; and (2) leveraging…

Automated Radiology Report Generation Based on Topic-Keyword Semantic Guidance
Jing Xiao, Hongfei Liu, Ruiqi Dong, Jimin Liu, Haoyong Yu
https://arxiv.org/abs/2509.10873 https:/…

Automated Radiology Report Generation Based on Topic-Keyword Semantic Guidance
Automated radiology report generation is essential in clinical practice. However, diagnosing radiological images typically requires physicians 5-10 minutes, resulting in a waste of valuable healthcare resources. Existing studies have not fully leveraged knowledge from historical radiology reports, lacking sufficient and accurate prior information. To address this, we propose a Topic-Keyword Semantic Guidance (TKSG) framework. This framework uses BiomedCLIP to accurately retrieve historical simi…

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Yongye Su, Zeya Zhang, Jane Kou, Cheng Ju, Shubhojeet Sarkar, Yamin Wang, Ji Liu, Shengbo Guo
https://arxiv.org/abs/2509.13603

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a framework of modernized Facebook Group Scoped Search by blending traditional keyword-based retrieval with embedding-based retrieval (EBR) to improve the search relevance and diversity of search results. Our system integrates semantic retrieval into the existing keyword search pipeline, enabling users to discover more…

finde dass python ein "praise" keyword braucht
um exceptions zu lobpreisen 🙏

IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators
Jinwoong Kim, Sangjin Park
https://arxiv.org/abs/2510.07661 https://

IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators
The increasing influence of unstructured external information, such as news articles, on stock prices has attracted growing attention in financial markets. Despite recent advances, most existing newsbased forecasting models represent all articles using sentiment scores or average embeddings that capture the general tone but fail to provide quantitative, context-aware explanations of the impacts of public sentiment on predictions. To address this limitation, we propose an interpretable keyword-g…

MAGIC-Enhanced Keyword Prompting for Zero-Shot Audio Captioning with CLIP Models
Vijay Govindarajan, Pratik Patel, Sahil Tripathi, Md Azizul Hoque, Gautam Siddharth Kashyap
https://arxiv.org/abs/2509.12591

MAGIC-Enhanced Keyword Prompting for Zero-Shot Audio Captioning with CLIP Models
Automated Audio Captioning (AAC) generates captions for audio clips but faces challenges due to limited datasets compared to image captioning. To overcome this, we propose the zero-shot AAC system that leverages pre-trained models, eliminating the need for extensive training. Our approach uses a pre-trained audio CLIP model to extract auditory features and generate a structured prompt, which guides a Large Language Model (LLM) in caption generation. Unlike traditional greedy decoding, our metho…

@… I wish it had a keyword or tag :-(
(Meaningless YouTube and webpage titles always turn me off … sorry.)

"Research on the construction and application of problem-method-oriented academic graph empowered by LLM" https://doi.org/10.1007/s10791-025-09675-2
"Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ lit…

Pro tip: If you’re creating Mermaid sequence diagrams, you can order the elements by using the `participant` keyword at the start of the diagram definition.

What Are #Python Asterisk and Slash Special Parameters For?
https://realpython.com/python-asterisk-and-slash-special-parameters/

Google says total organic click volume from Search to websites has been "relatively stable" YoY and it's sending "slightly more quality clicks" than a year ago (Liz Reid/The Keyword)
https://blog.google/products/search/ai-sear…

AI in Search is driving more queries and higher quality clicks
We continue to send billions of clicks to the web every day and are committed to prioritizing the web in our AI experiences in Search.

PSA: attrs 25.4 will change how it handles class-level kw_only to conform with dataclass transform rules (IOW act like type-checkers ex Mypy expect).
We found 0 cases in the wild where this would break anything so it’s on for NG APIs. Now is the time to stop us. 🤓
https://github.com/python-attrs/attrs/

Make `kw_only=True` behavior consistent with dataclasses by huzecong · Pull Request #1457 · python-attrs/attrs
Summary This PR resolve #980. Before this change When kw_only=True is set on a class, all attributes are made keyword-only, including those from base classes. If an attribute sets kw_only=False, th...

Google introduces Veo 3.1, with improved audio output and stronger prompt adherence, and rolls out new updates to its AI video editor Flow (The Keyword)
https://blog.google/technology/ai/veo-updates-flow/

Introducing Veo 3.1 and advanced capabilities in Flow
Today, we’re introducing new and enhanced creative capabilities to edit your clips.

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Pietro Bartoli, Tommaso Bondini, Christian Veronesi, Andrea Giudici, Niccol\`o Antonello, Franco Zappa
https://arxiv.org/abs/2509.07051

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers
Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment of AI-enabeld devices. In this work, we systematically evaluate and compare several state-of-the-art lightweight neural network architectures, including DS-CNN, LiCoNet, and TENet, alongside our proposed Typman-KWS (TKWS) architecture built upon MobileNet, specifically designed for efficient KWS on microcontroller unit…

Enhance the machine learning algorithm performance in phishing detection with keyword features
Zijiang Yang
https://arxiv.org/abs/2508.09765 https://arxiv.…

Enhance the machine learning algorithm performance in phishing detection with keyword features
Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive information and the financial loss for the end-users. To avoid such attacks, the early detection of these websites' URLs is vital and necessary. Previous researchers have proposed many machine learning a…

Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices
Yael Segal-Feldman, Ann R. Bradlow, Matthew Goldrick, Joseph Keshet
https://arxiv.org/abs/2508.04857 http…

Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices
Open-vocabulary keyword spotting (KWS) refers to the task of detecting words or terms within speech recordings, regardless of whether they were included in the training data. This paper introduces an open-vocabulary keyword spotting model with state-of-the-art detection accuracy for small-footprint devices. The model is composed of a speech encoder, a target keyword encoder, and a detection network. The speech encoder is either a tiny Whisper or a tiny Conformer. The target keyword encoder is i…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
Liang, Jia, Wang, Huang, Zhong, Xiang, Huang, Yuan

Google releases Cell2Sentence-Scale 27B (C2S-Scale), a 27B-parameter foundation model for single-cell analysis built on its Gemma family of open models (The Keyword)
https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/

How a Gemma model helped discover a new potential cancer therapy pathway
We’re launching a new 27 billion parameter foundation model for single-cell analysis built on the Gemma family of open models.

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Chin Yuen Kwok, Jia Qi Yip, Eng Siong Chng
https://arxiv.org/abs/2509.09197 ht…

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Rare word recognition can be improved by adapting ASR models to synthetic data that includes these words. Further improvements can be achieved through contextual biasing, which trains and adds a biasing module into the model architecture to prioritize rare words. While training the module on synthetic rare word data is more effective than using non-rare-word data, it can lead to overfitting due to artifacts in the synthetic audio. To address this, we enhance the TCPGen-based contextual biasing …

Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
Huihong Liang, Dongxuan Jia, Youquan Wang, Longtao Huang, Shida Zhong, Luping Xiang, Lei Huang, Tao Yuan
https://arxiv.org/abs/2509.06964

Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
In this demo, we present a compact intelligent audio system-on-chip (SoC) integrated with a keyword spotting accelerator, enabling ultra-low latency, low-power, and low-cost voice interaction in Internet of Things (IoT) devices. Through algorithm-hardware co-design, the system's energy efficiency is maximized. We demonstrate the system's capabilities through a live FPGA-based prototype, showcasing stable performance and real-time voice interaction for edge intelligence applications.

#dotnet prediction for 2026: You'll see a re-emphasis on the "dynamic" keyword to appeal to Python folks.

Crosslisted article(s) found for cs.AR. https://arxiv.org/list/cs.AR/new
[1/1]:
- Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
Liang, Jia, Wang, Huang, Zhong, Xiang, Huang, Yuan

LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
Shaoxiong Zhan, Hai Lin, Hongming Tan, Xiaodong Cai, Hai-Tao Zheng, Xin Su, Zifei Shan, Ruitong Liu, Hong-Gee Kim
https://arxiv.org/abs/2508.17858

LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrated strong performance in semantic matching. Nevertheless, they often struggle with fine-grained retrieval tasks, where precise keyword alignment and span-level localization are required, even in cases with high lexical overlap that would intuitively suggest easier retrieval. To systematically evaluate this limitation,…

Crosslisted article(s) found for physics.ed-ph. https://arxiv.org/list/physics.ed-ph/new
[1/1]:
- Updating the Complex Systems Keyword Diagram Using Collective Feedback and Latest Literature Data
Hiroki Sayama

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
Pei Liu, Terry Zhuo, Jiawei Deng, Zhenchang Xing, Qinghua Lu, Xiaoning Du, Hongyu Zhan
https://arxiv.org/abs/2508.11179

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
The rapid emergence of pretrained models (PTMs) has attracted significant attention from both Deep Learning (DL) researchers and downstream application developers. However, selecting appropriate PTMs remains challenging because existing methods typically rely on keyword-based searches in which the keywords are often derived directly from function descriptions. This often fails to fully capture user intent and makes it difficult to identify suitable models when developers also consider factors s…

xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning
Cheng Qian, Zuxin Liu, Shirley Kokane, Akshara Prabhakar, Jielin Qiu, Haolin Chen, Zhiwei Liu, Heng Ji, Weiran Yao, Shelby Heinecke, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2510.08439

xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning
Modern LLM deployments confront a widening cost-performance spectrum: premium models deliver strong reasoning but are expensive, while lightweight models are economical yet brittle on complex tasks. Static escalation rules and keyword heuristics under-utilize this spectrum and fail to adapt across task types. We present xRouter, a tool-calling-based routing system in which a learned router can either answer directly or invoke one or more external models. The router is trained end-to-end with re…

Be honest, who here remembers that #csharp introduced a file keyword? #dotnet

Google releases the Gemini 2.5 Computer Use model, built on Gemini 2.5 Pro's capabilities to power agents that can interact with UIs, in preview via the API (The Keyword)
https://blog.google/technology/google-deepmind/gemini-computer-use-model/

Introducing the Gemini 2.5 Computer Use model
Today we are releasing the Gemini 2.5 Computer Use model via the API, which outperforms leading alternatives at browser and mobile tasks.

Optimizing Product Deduplication in E-Commerce with Multimodal Embeddings
Aysenur Kulunk, Berk Taskin, M. Furkan Eseoglu, H. Bahadir Sahin
https://arxiv.org/abs/2509.15858 https…

Optimizing Product Deduplication in E-Commerce with Multimodal Embeddings
In large scale e-commerce marketplaces, duplicate product listings frequently cause consumer confusion and operational inefficiencies, degrading trust on the platform and increasing costs. Traditional keyword-based search methodologies falter in accurately identifying duplicates due to their reliance on exact textual matches, neglecting semantic similarities inherent in product titles. To address these challenges, we introduce a scalable, multimodal product deduplication designed specifically f…

Shaping Explanations: Semantic Reward Modeling with Encoder-Only Transformers for GRPO
Francesco Pappone, Ruggero Marino Lazzaroni, Federico Califano, Niccol\`o Gentile, Roberto Marras
https://arxiv.org/abs/2509.13081

Shaping Explanations: Semantic Reward Modeling with Encoder-Only Transformers for GRPO
While Large Language Models (LLMs) excel at generating human-like text, aligning their outputs with complex, qualitative goals like pedagogical soundness remains a significant challenge. Standard reinforcement learning techniques often rely on slow and expensive LLM-as-a-judge evaluations or on brittle, keyword-based metrics like ROUGE, which fail to capture the semantic essence of a high-quality explanation. In this work, we introduce a novel approach to reward shaping within the Group Relativ…

Google says total organic click volume from Search to websites has been "relatively stable" YoY and it's sending "slightly more quality clicks" than a year ago (Liz Reid/The Keyword)
https://blog.google/products/search/ai-sear…

AI in Search is driving more queries and higher quality clicks
We continue to send billions of clicks to the web every day and are committed to prioritizing the web in our AI experiences in Search.

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre, Mantinder Singh, Ashok Jallepalli, Hritvik Shrivastava, Pradeep Kumar Sharma
https://arxiv.org/abs/2510.08850

Repository-Aware File Path Retrieval via Fine-Tuned LLMs
Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the bug introduced?" Traditional code search (keyword or IR based) often misses semantic context and cross file links, while large language models (LLMs) understand natural language but lack repository specific detail. We present a method for file path retrieval that fine tunes a strong LLM (Qwen3-8B) with QLoRA and Unslot…

An automatic patent literature retrieval system based on LLM-RAG
Yao Ding, Yuqing Wu, Ziyang Ding
https://arxiv.org/abs/2508.14064 https://arxiv.org/pdf/25…

An automatic patent literature retrieval system based on LLM-RAG
With the acceleration of technological innovation efficient retrieval and classification of patent literature have become essential for intellectual property management and enterprise RD Traditional keyword and rulebased retrieval methods often fail to address complex query intents or capture semantic associations across technical domains resulting in incomplete and lowrelevance results This study presents an automated patent retrieval framework integrating Large Language Models LLMs with Retri…

Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
Mustapha Adamu, Qi Zhang, Huitong Pan, Longin Jan Latecki, Eduard C. Dragut
https://arxiv.org/abs/2509.10087

Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
The growing complexity and volume of climate science literature make it increasingly difficult for researchers to find relevant information across models, datasets, regions, and variables. This paper introduces a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts, aimed at improving how climate knowledge is accessed and used. Unlike keyword based search, our KG supports structured, semantic queries that help researchers discover precise connections…

PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting
Bronya Roni Chernyak, Yael Segal, Yosi Shrem, Joseph Keshet
https://arxiv.org/abs/2508.03190

PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting
Deep learning models excel at many tasks but rely on the assumption that training and test data follow the same distribution. This assumption often does not hold in real-world speech systems, where distribution shifts are common due to varying environments, recording conditions, and speaker diversity. The method of Domain Shifts with Uncertainty (DSU) augments the input of each neural network layer based on the input feature statistics. It addresses the problem of out-of-domain generalization…

More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research
Trent D. Buskirk, Florian Keusch, Leah von der Heyde, Adam Eck
https://arxiv.org/abs/2509.03391

More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research
Survey research has a long-standing history of being a human-powered field, but one that embraces various technologies for the collection, processing, and analysis of various behavioral, political, and social outcomes of interest, among others. At the same time, Large Language Models (LLMs) bring new technological challenges and prerequisites in order to fully harness their potential. In this paper, we report work-in-progress on a systematic literature review based on keyword searches from mult…

Keywords are not always the key: A metadata field analysis for natural language search on open data portals
Lisa-Yao Gan, Arunav Das, Johanna Walker, Elena Simperl
https://arxiv.org/abs/2509.14457

Keywords are not always the key: A metadata field analysis for natural language search on open data portals
Open data portals are essential for providing public access to open datasets. However, their search interfaces typically rely on keyword-based mechanisms and a narrow set of metadata fields. This design makes it difficult for users to find datasets using natural language queries. The problem is worsened by metadata that is often incomplete or inconsistent, especially when users lack familiarity with domain-specific terminology. In this paper, we examine how individual metadata fields affect the…

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Grazia Sveva Ascione, Nicol\`o Tamagnone
https://arxiv.org/abs/2509.09303

From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models
Classifying patents by their relevance to the UN Sustainable Development Goals (SDGs) is crucial for tracking how innovation addresses global challenges. However, the absence of a large, labeled dataset limits the use of supervised learning. Existing methods, such as keyword searches, transfer learning, and citation-based heuristics, lack scalability and generalizability. This paper frames patent-to-SDG classification as a weak supervision problem, using citations from patents to SDG-tagged sci…

Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu
https://arxiv.org/abs/2509.09683 https://

Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is use…

Demonstrating Narrative Pattern Discovery from Biomedical Literature
Hermann Kroll, Pascal Sackhoff, Bill Matthias Thang, Christin Katharina Kreutz, Wolf-Tilo Balke
https://arxiv.org/abs/2509.09687

Demonstrating Narrative Pattern Discovery from Biomedical Literature
Digital libraries maintain extensive collections of knowledge and need to provide effective access paths for their users. For instance, PubPharm, the specialized information service for Pharmacy in Germany, provides and develops access paths to their underlying biomedical document collection. In brief, PubPharm supports traditional keyword-based search, search for chemical structures, as well as novel graph-based discovery workflows, e.g., listing or searching for interactions between different…

Crosslisted article(s) found for eess.AS. https://arxiv.org/list/eess.AS/new
[1/1]:
- Prototype: A Keyword Spotting-Based Intelligent Audio SoC for IoT
Liang, Jia, Wang, Huang, Zhong, Xiang, Huang, Yuan

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng, Mingyang Liu, Peijun Zheng, Jie Liu, Dapeng Wu, Yinqiao Li, Linqi Song
https://arxiv.org/abs/2509.07860

KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching. These approaches are often inefficient and struggle to reveal the complex relationships hidden within large patent datasets, hindering strategic decision-making. To overcome these limitations, we introduce KLIPA, a novel framework that leverages a knowledge graph and a large language model (LLM) to significantly adva…

Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Support
Hei Yu Chan, Kuok Tou Ho, Chenglong Ma, Yujing Si, Hok Lai Lin, Sa Lei Lam
https://arxiv.org/abs/2508.05664

Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Support
Many AI customer service systems use standard NLP pipelines or finetuned language models, which often fall short on ambiguous, multi-intent, or detail-specific queries. This case study evaluates recent techniques: query rewriting, RAG Fusion, keyword augmentation, intent recognition, and context reranking, for building a robust customer support system in the electric power domain. We compare vector-store and graph-based RAG frameworks, ultimately selecting the graph-based RAG for its superior p…

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Yunfei Zhong, Jun Yang, Yixing Fan, Jiafeng Guo, Lixin Su, Maarten de Rijke, Ruqing Zhang, Dawei Yin, Xueqi Cheng
https://arxiv.org/abs/2509.06544

Reasoning-enhanced Query Understanding through Decomposition and Interpretation
Accurate inference of user intent is crucial for enhancing document retrieval in modern search engines. While large language models (LLMs) have made significant strides in this area, their effectiveness has predominantly been assessed with short, keyword-based queries. As AI-driven search evolves, long-form queries with intricate intents are becoming more prevalent, yet they remain underexplored in the context of LLM-based query understanding (QU). To bridge this gap, we introduce ReDI: a Reaso…

HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
Alejandro Godinez
https://arxiv.org/abs/2508.05666

HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
We present HySemRAG, a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG) to automate large-scale literature synthesis and identify methodological research gaps. The system addresses limitations in existing RAG architectures through a multi-layered approach: hybrid retrieval combining semantic search, keyword filtering, and knowledge graph traversal; an agentic self-correction framework with iterative quality assurance; and post-hoc citati…