Tootfinder

Metacurity is pleased to offer our free and premium subscribers this weekly digest of the best long-form (and longish) infosec-related pieces we couldn’t properly fit into our daily news crush.
This week's selection covers
--The Russian consultant who pumps out disinformation,
--The expert who disproved the MyPillow guy is still waiting for his money,
--China tries to ramp up disinformation with AI news anchors,
--Crypto scam tweets struggle to find victims,
--Replacing public cryptography keys with something better,
--NYC to test error-prone gunshot detection system
https://www.metacurity.com/p/best-infosecrelated-long-reads-week-51124

This https://arxiv.org/abs/2402.14301 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GenSERP: Large Language Models for Whole Page Presentation
The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a framework that leverages LLMs with vision in a few-shot setting to dynamically organize intermediate search results, including generated chat answers, website snippets, multimedia data, knowledge panels into a coherent SERP layout based on a user's query. Our approach has three main stages: (1) An information gathering pha…

i never liked St*ck O*erflow's br*-ish culture so i never participated. People were encouraged to ruthlessly downvote others' answers in the name of logic and being "right" with minimal terse language without any empathy ; we are h*mans not V*lcans :-) #Ymmv

London: Russland wirbt weiter Ausländer für Armee an
Russland wirbt im Angriffskrieg gegen die Ukraine nach britischen Angaben weiter um ausländische Soldaten. In einer englischsprachigen Broschüre würden Ausländer aufgefordert, sich einer Spezialeinheit anzuschließen, teilte das britische Verteidigungsministerium mit. Geboten würden 2.000 US-Dollar (1.880 Euro) Prämie sowie 2.200 Dollar pro Monat s…
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Ministry of Defence 🇬🇧 (@DefenceHQ) on X
Latest Defence Intelligence update on the situation in Ukraine – 15 April 2024. Find out more about Defence Intelligence's use of language: https://t.co/EwBELWpRTX #StandWithUkraine 🇺🇦

ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
David Anugraha, Genta Indra Winata, Chenyue Li, Patrick Amadeus Irawan, En-Shiun Annie Lee
https://arxiv.org/abs/2406.09334

ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
Performance prediction is a method to estimate the performance of multilingual language models (LMs), mitigating computational costs associated with model capacity and data for fine-tuning. Our paper introduces ProxyLM, a scalable framework for predicting LM performance using proxy models in multilingual tasks. These proxy models act as surrogates, approximating the performance of fine-tuned LMs on specific downstream natural language processing (NLP) tasks. By leveraging proxy models, ProxyLM …

This https://arxiv.org/abs/2404.04264 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accurac…

InfiCoder-Eval: Systematically Evaluating the Question-Answering Capabilities of Code Large Language Models
Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang
https://arxiv.org/abs/2404.07940

InfiCoder-Eval: Systematically Evaluating the Question-Answering Capabilities of Code Large Language Models
Large Language Models for understanding and generating code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-r…

Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
Sarah Ball, Frauke Kreuter, Nina Rimsky
https://arxiv.org/abs/2406.09289

Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
Conversational Large Language Models are trained to refuse to answer harmful questions. However, emergent jailbreaking techniques can still elicit unsafe outputs, presenting an ongoing challenge for model alignment. To better understand how different jailbreak types circumvent safeguards, this paper analyses model activations on different jailbreak inputs. We find that it is possible to extract a jailbreak vector from a single class of jailbreaks that works to mitigate jailbreak effectiveness f…

Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, Yingfei Sun
https://arxiv.org/abs/2404.10496

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min zhang
https://arxiv.org/abs/2406.09072

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748…