Papers with URLs
Ignore Me But Don’t Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain (2024.findings-naacl)
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| Challenge: | Existing methods to train domain expertise for cybersecurity text domains are expensive to train and run. |
| Approach: | They propose to use pretraining methods to account for non-linguistic elements in cybersecurity texts and evaluate their effectiveness through downstream tasks and probing tasks. |
| Outcome: | The proposed strategy outperforms the commonly taken approach of replacing NLEs and outperformed other cybersecurity PLMs on most tasks. |
Search Query Embeddings via User-behavior-driven Contrastive Learning (2025.naacl-industry)
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Sosuke Nishikawa, Jun Hirako, Nobuhiro Kaji, Koki Watanabe, Hiroki Asano, Souta Yamashiro, Shumpei Sano
| Challenge: | Existing approaches to embed search queries are limited due to shortness and surface-level variations. |
| Approach: | They propose a user-behavior-driven contrastive learning approach which directly aligns query embeddings according to user intent. |
| Outcome: | The proposed model outperforms state-of-the-art text embedding models on real-world QU tasks while minimizing lexical similarities. |
Assessing In-context Learning and Fine-tuning for Topic Classification of German Web Data (2024.acl-srw)
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| Challenge: | Using a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. |
| Approach: | They propose to use annotated data to train a binary classification task to detect topic-related content in a scraped database of webpages. |
| Outcome: | The proposed model detects content related to three German policies in a scraped database of scrapes of webpages using a few hundred annotated data points per topic. |
Benchmarking Deep Search over Heterogeneous Enterprise Data (2025.emnlp-industry)
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Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath, Kung-Hsiang Huang, Caiming Xiong, Chien-Sheng Wu
| Challenge: | Existing methods struggle to conduct deep searches and retrieve all necessary evidence. |
| Approach: | They propose a benchmark for evaluating deep search, a retrieval-augmented generation that requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources. |
| Outcome: | The proposed benchmarks show that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on the benchmark. |
MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection (2026.acl-industry)
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| Challenge: | Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. |
| Approach: | They propose a memory-augmented multi-modal LLM agent that leverages episodic memories to guide decisions on recurring and novel threats. |
| Outcome: | The proposed agent outperforms state-of-the-art phishing detection tools on two public datasets and improves recall by 20%. |
Large Language Models are Built-in Autoregressive Search Engines (2023.findings-acl)
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| Challenge: | Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing only shallow interactions between them. |
| Approach: | They propose to use large language models to generate URLs for document retrieval by following human instructions. |
| Outcome: | The proposed method achieves better retrieval performance than existing retrieval approaches on open-domain question answering benchmarks. |
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs (2020.emnlp-main)
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| Challenge: | Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. |
| Outcome: | The proposed method can label documents at 94.5% across languages with high precision . the proposed method is useful for low-resource languages with limited resources . |
Continuous Decomposition of Granularity for Neural Paraphrase Generation (2022.coling-1)
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| Challenge: | Prior work has shown that decomposing sentences at different levels of granularity has improved paragraph generation. |
| Approach: | They propose a model for continuous decomposing granularity for neural paraphrase generation that incorporates granules into attention. |
| Outcome: | The proposed model outperforms baseline models on Quora question pairs and Twitter URLs on two benchmarks. |
MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs (2026.findings-acl)
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Dezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han
| Challenge: | Existing models struggle to detect elaborately disguised malicious URLs, despite their ability to process malicious URL's. |
| Approach: | They propose a benchmark to evaluate LLMs’ vulnerabilities to malicious URLs and a lightweight defense module to mitigate the vulnerability. |
| Outcome: | The proposed framework analyzes 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. |
CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models suffer from paraphrasing errors, omissions, or hallucinations when input contains translation-specific elements that require strict preservation or controlled transformation. |
| Approach: | They propose a Controllable Element-Oriented Machine Translation framework that decomposes the translation process into a linguistically grounded analysis, strategy formulation, and final generation. |
| Outcome: | The proposed framework improves on the WMT23/24 Chinese–English benchmarks while significantly reducing element-level constraint violations. |