Law
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.
Supplementary File for ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Evaluation Capability for Large Vision-Language Models
We calculate the agreement of human judgment and our automatic evaluation (i.e., ConvBenchEval()) and find it reaches 81.83% (seeing Table 3 - 6 for detailed agreement of each turn of overall). It demonstrates the effectiveness of ConvBenchEval(), which uses ChatGPT. The agreement between ChatGPT and GPT4 is very high at 87.38%. It demonstrates that using different LLMs as judges slightly influences the evaluation results. ConvBenchEval() armed with ChatGPT can is reliable and low-cost. From the above tables, we also observe that though GPT4V is expensive and can capture images, its judgment performs worse than GPT4's judgment.
ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Ablation Capability for Large Vision-Language Models
Multi-turn visual conversation is an important ability of real-world AI assistants. However, the related evaluation benchmark is missed.
Streaming Detection of Queried Event Start
Natural language, in contrast, enables users to flexibly specify complex events.
