For Open-domain QA we report test numbers using 15 retrieved documents for RAG-Token models. For RAG-Sequence models, we report test results using 50 retrieved documents, and we use the Thorough Decoding approach since answers are generally short. We use greedy decoding for QA as we did not find beam search improved results. For Open-MSMarco and Jeopardy question generation, we report test numbers using ten retrieved documents for both RAG-Token and RAG-Sequence, and we also train a BART-large model as a baseline. We use a beam size of four, and use the Fast Decoding approach for RAG-Sequence models, as Thorough Decoding did not improve performance.

Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current efficient answer correctness (AC) metrics do not align with human judgments, particularly verbose, free-form answers from large language models (LLMs). There are two challenges: a lack of diverse evaluation data and that models are too big and non-transparent; LLM-based scorers correlate better with humans, but this expensive task has only been tested on limited QA datasets. We rectify these issues by providing guidelines and datasets for evaluating machine QA adopted from human QA community. We also propose an efficient, low-resource, and interpretable QA evaluation method more stable than an exact match and neural methods.

We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a website, which is represented as a graph consisting of web pages as nodes and hyperlinks as directed edges, to find a web page in which a query appears. The agent is required to have sophisticated high-level reasoning based on natural languages and efficient sequential decision-making capability to succeed. We release a software tool, called WebNav, that automatically transforms a website into this goal-driven web navigation task, and as an example, we make WikiNav, a dataset constructed from the English Wikipedia. We extensively evaluate different variants of neural net based artificial agents on WikiNav and observe that the proposed goal-driven web navigation well reflects the advances in models, making it a suitable benchmark for evaluating future progress. Furthermore, we extend the WikiNav with questionanswer pairs from Jeopardy! and test the proposed agent based on recurrent neural networks against strong inverted index based search engines. The artificial agents trained on WikiNav outperforms the engined based approaches, demonstrating the capability of the proposed goal-driven navigation as a good proxy for measuring the progress in real-world tasks such as focused crawling and question-answering.

Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to determine answer equivalence (AE) often do not align with human judgments, particularly more verbose, free-form answers from large language models (LLM). There are two challenges: a lack of data and that models are too big: LLM-based scorers can correlate better with human judges, but this task has only been tested on limited QA datasets, and even when available, update of the model is limited because LLMs are large and often expensive. We rectify both of these issues by providing clear and consistent guidelines for evaluating AE in machine QA adopted from professional human QA contests. We also introduce a combination of standard evaluation and a more efficient, robust, and lightweight discriminate AE classifier-based matching method (CFMatch, smaller than 1 MB), trained and validated to more accurately evaluate answer correctness in accordance with adopted expert AE rules that are more aligned with human judgments.

In response to concerns in the classroom, a fleet of companies have released products claiming they can flag AI generated text. A Post examination showed it can wrongly flag human generated text as written by AI. In January, ChatGPT-maker OpenAI said it created a tool that can distinguish between human and AI-generated text, but noted that it "is not fully reliable" and is wrong 9 percent of the time.

In early 2011, Ken Jennings looked like humanity's last hope. Watson, an artificial intelligence created by the tech giant IBM, had picked off lesser Jeopardy players before the show's all-time champ entered a three-day exhibition match. At the end of the first game, Watson--a machine the size of 10 refrigerators--had Jennings on the ropes, leading $35,734 to $4,800. On day three, Watson finished the job. "I for one welcome our new computer overlords," Jennings wrote on his video screen during Final Jeopardy. Watson was better than any previous AI at addressing a problem that had long stumped researchers: How do you get a computer to precisely understand a clue posed in idiomatic English and then spit out the correct answer (or, as in Jeopardy, the right question)?

'Gutfeld!' guests discuss a Jeopardy question that used alleged murderer Brian Laundrie as the clue. A "Jeopardy!" contestant is going viral this week after making what many fans are considering one of the biggest blunders in the show's history. On Wednesday's episode, a woman named Karen had a huge lead over the other two contestants as they neared the end of the second round – she had earned $21,800, while her competitors had earned $7,100 and $6,400. When there were only a few clues left on the Double Jeopardy board, Karen found a Daily Double in the "Hans, Solo" category. If she had made a modest bet, she would have been sure to win the entire game after Final Jeopardy, as the other players couldn't possibly catch up to her lead.

In simple terms the AI or the Artificial Intelligence means the replicating the Human Intelligence. In deeply the artificial intelligence is a large concept that spread through a huge domain. Actually I assume there is no domain when we come to the AI, because it spread in each and every domain that exists. So, the artificial intelligence is the theory and development of computer systems with the ability to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making and translation between languages. Also there are another two concepts that goes with AI very closely.

When we include the unprecedented computing power offered by the cloud, it's clear we are living in an exciting era for building applications. When IBM Watson defeated the two Jeopardy champions back in 2011, it opened a new era in the practical application of Artificial Intelligence technology and contributed to the growing research and interest in this field. IBM Watson has evolved from being a game show winning question & answering computer system to a set of enterprise-grade artificial intelligence (AI) application program interfaces (API) available on IBM Cloud. These Watson APIs can ingest, understand & analyze all forms of data, allow for natural forms of interactions with people, learn, reason – all at a scale that allows for business processes and applications to be reimagined. This course is intended for business and technical users who want to learn more about the cognitive capabilities of IBM Watson Discovery service.