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BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
To bridge this gap, we introduce the BABILong benchmark, designed to test language models' ability to reason across facts distributed in extremely
RedCode: Risky Code Execution and Generation Benchmark for Code Agents
We then map the Python code to other programming languages (e.g., Bash) and natural text summaries or descriptions for
A Supplementary materials
The array geometry is shown in the RealMAN paper.
Large Language Model Unlearning
We study how to perform unlearning, i.e. forgetting undesirable (mis)behaviors, on large language models (LLMs).
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models Hannah Rose Kirk 1
Human feedback is central to the alignment of Large Language Models (LLMs).
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
Satellite-based remote sensing has revolutionised the way we address global challenges in a rapidly evolving world. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but processing these large datasets for use in ML pipelines is technically and computationally challenging. Specifically, different types of EO data are often hosted on a variety of platforms, with differing degrees of availability for Python preprocessing tools. In addition, spatial alignment across data sources and data tiling for easier handling can present significant technical hurdles for novice users.
Stepwise Alignment for Constrained Language Model Policy Optimization Akifumi Wachi Thien Q. Tran Rei Sato Takumi Tanabe Y ouhei Akimoto L Y Corporation University of Tsukuba
Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).
Supplementary Material Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline Qi Jia 1 Baoyu Fan 2,1 Cong Xu1 Lu Liu
This section provides a comprehensive overview of the CSMV dataset. This extensive time range allows for the inclusion of a diverse set of content, capturing the evolution of sentiments over the course of more than two years. The distribution of labels in our CSMV dataset is shown in Figure 1. In Figure 1a, the opinion labels are distributed as follows: positive - 47%, neutral - 42%, and negative - 11%. Negative comments are clearly in the minority.
Private Attribute Inference from Images with Vision-Language Models
As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus.
Google Search Could Change Forever in the UK
Google may be forced to make major changes in the way that people use its search engine in the UK. Google may have to change the way its search engine works in the UK, including potentially offering users the option to choose rival search services, as part of new regulation from the UK's competition authority. In a decision handed down on Friday, the Competition and Markets Authority (CMA) has designated Google Search with Strategic Market Status (SMS)--a qualifier given to companies that are considered to have "substantial and entrenched market power"--which would allow the regulator to wield more power over it. This decision follows a 10-month investigation into Google, and it is the first time that these powers, which come under the UK's new Digital Markets, Competition and Consumers Act, have been used to target a major tech company. Google's SMS will last up to five years under this legislation.