Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations
The robustness of recent Large Language Models (LLMs) has become increasingly crucial
as their applicability expands across various domains and real-world applications. Retrieval …
as their applicability expands across various domains and real-world applications. Retrieval …
Eyes on Google's NotebookLM: using generative AI to create ophthalmology podcasts with a single click
NotebookLM is a new and exciting artificial intelligence (AI)-powered research assistant by
Google that learns from useruploaded multimodal information—such as documents, images …
Google that learns from useruploaded multimodal information—such as documents, images …
MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models
Large language models (LLMs) are commonly used as evaluators in tasks (eg, reward
modeling, LLM-as-a-judge), where they act as proxies for human preferences or judgments …
modeling, LLM-as-a-judge), where they act as proxies for human preferences or judgments …
Semiparametric Token-Sequence Co-Supervision
In this work, we introduce a semiparametric token-sequence co-supervision training method.
It trains a language model by simultaneously leveraging supervision from the traditional next …
It trains a language model by simultaneously leveraging supervision from the traditional next …
DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI
Large language models (LLMs) often struggle to objectively identify latent characteristics in
large datasets due to their reliance on pre-trained knowledge rather than actual data …
large datasets due to their reliance on pre-trained knowledge rather than actual data …