Summarization

While Microsoft and Google scramble to supercharge their businesses with text generation, Meta has yet to launch a flagship generative AI service. Reporters went looking for reasons why.

The crowdworkers you hire to provide human data may use AI to produce it. Researchers at École Polytechnique Fédérale de Lausanne found that written material supplied by workers hired via Amazon Mechanical Turk showed signs of being generated by ChatGPT.

What do your GitHub projects reveal about your professional prospects? A new model aims to help recruiters find out. Prog.ai analyzes GitHub repositories to help employers find engineers skilled in particular areas, TechCrunch reported.

Researchers pushed the boundaries of language models to address persistent problems of trustworthiness, bias, and updatability.

Machine learning is helping lawyers sift through mountains of documents to find evidence. The legal technology company Everlaw launched a clustering feature that automatically organizes up to 25 million documents for lawyers gathering evidence to be used during a trial.

A new summarization model boils down AI research papers to a single sentence. TLDR from Allen Institute for AI creates at-a-glance summaries of scientific research papers. It’s up and running at Semantic Scholar, a research database, where searches now return its pithy precis.

Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.

Ignorance is a choice in the Internet age. Virtually all of human knowledge is available for the cost of typing a few words into a search box.

Earlier language models powered by Word2Vec and GloVe embeddings yielded confused chatbots, grammar tools with middle-school reading comprehension, and not-half-bad translations. The latest generation is so good, some people consider it dangerous.

Automatically generated text summaries are becoming common in search engines and news websites. But existing summarizers often mix up facts. For instance, a victim’s name might get switched for the perpetrator’s.

Models that summarize documents and answer questions work pretty well with limited source material, but they can slip into incoherence when they draw from a sizeable corpus. Recent work addresses this problem.

Summarizing a document using original words is a longstanding problem for natural language processing. Researchers recently took a step toward human-level performance in this task, known as abstractive summarization, as opposed to extractive summarization.

The latest language models are great at answering questions about a given text passage. However, these models are also powerful enough to recognize an individual writer’s style, which can clue them in to the right answers. New research measures such annotator bias in several data sets.

Watson set a high bar for language understanding in 2011, when it famously whipped human competitors in the televised trivia game show Jeopardy! IBM’s special-purpose AI required around $1 billion. Research suggests that today’s best language models can accomplish similar tasks right off the shelf.

A growing number of companies that sell standardized tests are using natural language processing to assess writing skills. Critics contend that these language models don’t make the grade.