Google Bard read online pdf works now (2024.02)

Yes. I can access and read articles from online websites through Google search. I can also understand and answer questions about these articles.

For example, I can read any article on Wikipedia: URL Wikipedia. I can provide a summary of the article's topic, highlight key points, and answer specific questions about the content.

I can also read articles from news websites, blogs, and other online publications. I can provide a summary of the article's topic, highlight key points, and answer specific questions about the content.

I can also read articles from academic journals. I can provide a summary of the article's topic, highlight key points, and answer specific questions about the content.

Here are some examples of online articles I can read:

- Wikipedia: URL Wikipedia's article on "American History"
- The New York Times: URL New York Times' article on "The State of the American Economy"
- Science: URL Science's article on "The Science of Climate Change"

If you have any questions about online articles I can read, feel free to ask.

Of course. Here is the summary of the paper **READTWICE: Reading Very Large Documents with Memories**:

**Abstract**

Knowledge-intensive tasks, such as question answering, often require assimilating information from different sections of large inputs like books or collections of articles. We propose a simple and effective technique called **READTWICE**, which combines several strengths of prior approaches to model long-range dependencies with Transformers. The main idea is to read text in small segments in parallel, summarizing each segment into a memory table to be used in a second read of the text. expand_more

**Method**

The READTWICE model architecture is shown in the figure below:

READTWICE Model Architecture: [Removed invalid URL]

The model divides the input text into multiple small segments. Each segment is encoded independently by a Transformer encoder. The encoder's output is fed into an aggregator, which summarizes them into a memory table. The memory table is then used for a second read of the entire text.

**Results**

We evaluated the READTWICE model on several question-answering datasets. The results show that the model outperforms models of comparable size in several aspects:

- READTWICE has higher accuracy on question-answering datasets like SQuAD and TriviaQA.
- READTWICE performs better on tasks like NarrativeQA that require reasoning over long texts.

**Conclusion**

READTWICE is a simple and effective technique for modeling long-range dependencies with Transformers. expand_more The model has achieved state-of-the-art results on several question-answering datasets.

**Keywords**

- Question Answering
- Long Text
- Transformer
- Memory

**References**

- READTWICE: Reading Very Large Documents with Memories: [https://arxiv.org/abs/2105.04241](https://arxiv.org/abs/2105.04241)