Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first. In this paper we devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and a problem solving phase and show that the strategy is able to outperform a single stage solution. Further, we hypothesize that the decomposition should be easier to distill into a smaller model compared to the problem solving because the latter requires large amounts of domain knowledge while the former only requires learning general problem solving strategies. We propose methods to distill these two capabilities and evaluate their impact on reasoning outcomes and inference cost. We find that we can distill the problem decomposition phase and at the same time achieve good generalization across tasks, datasets, and models. However, it is harder to distill the problem solving capability without losing performance and the resulting distilled model struggles with generalization. These results indicate that by using smaller, distilled problem decomposition models in combination with problem solving LLMs we can achieve reasoning with cost-efficient inference and local adaptation.

Knowledge Bases (KBs) provide structured representation of the real-world in the form of extensive collections of facts about real-world entities, their properties and relationships. They are ubiquitous in large-scale intelligent systems that exploit structured information such as in tasks like structured search, question answering and reasoning, and hence their data quality becomes paramount. The inevitability of change in the real-world, brings us to a central property of KBs -- they are highly dynamic in that the information they contain are constantly subject to change. In other words, KBs are unstable. In this paper, we investigate the notion of KB stability, specifically, the problem of KBs changing due to real-world change. Some entity-property-pairs do not undergo change in reality anymore (e.g., Einstein-children or Tesla-founders), while others might well change in the future (e.g., Tesla-board member or Ronaldo-occupation as of 2022). This notion of real-world grounded change is different from other changes that affect the data only, notably correction and delayed insertion, which have received attention in data cleaning, vandalism detection, and completeness estimation already. To analyze KB stability, we proceed in three steps. (1) We present heuristics to delineate changes due to world evolution from delayed completions and corrections, and use these to study the real-world evolution behaviour of diverse Wikidata domains, finding a high skew in terms of properties. (2) We evaluate heuristics to identify entities and properties likely to not change due to real-world change, and filter inherently stable entities and properties. (3) We evaluate the possibility of predicting stability post-hoc, specifically predicting change in a property of an entity, finding that this is possible with up to 83% F1 score, on a balanced binary stability prediction task.

We argue that the next frontier in natural language understanding (NLU) and generation (NLG) will include models that can efficiently access external structured knowledge repositories. In order to support the development of such models, we release the VisualSem knowledge graph (KG) which includes nodes with multilingual glosses and multiple illustrative images and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural network) model pipeline and we encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval model are publicly available and can be downloaded in: https://github.com/iacercalixto/visualsem.

Pointer generator networks have been used successfully for abstractive summarization. Along with the capability to generate novel words, it also allows the model to copy from the input text to handle out-of-vocabulary words. In this paper, we point out two key shortcomings of the summaries generated with this framework via manual inspection, statistical analysis and human evaluation. The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries. The second shortcoming is the factual inaccuracies in the generated text despite grammatical correctness. Our analysis indicates that this arises due to incorrect attention transition between different parts of the article. We propose an initial attempt towards addressing both these shortcomings by externally appending traditional linguistic information parsed from the input text, thereby teaching networks on the structure of the underlying text. Results indicate feasibility and potential of such additional cues for improved generation.

The sportswear giant Nike, which has a contract reported to be worth $1bn (£768m) with Cristiano Ronaldo, says it is "deeply concerned" by rape allegations against the footballer. It adds that it will "closely monitor" the situation. Ronaldo, who plays for Juventus and Portugal, denies assaulting former teacher Kathryn Mayorga at a Las Vegas hotel in 2009. Ms Mayorga has said she was inspired to speak out by the #MeToo movement. Meanwhile, another Ronaldo sponsor, the games company EA Sports, called the report detailing allegations against him "concerning".

Brazil forward Neymar said he needed a new challenge, as he joined Paris St-Germain from Barcelona for a world record fee of 222m euros (£200m). The 25-year-old won seven major trophies in his four seasons at the Nou Camp, including the Champions League once and La Liga twice. He said his father, Neymar Sr, wanted him to stay at Barcelona. "I have won all that a player can win," said Neymar, who will earn 45m euros (£40.7m) a year on a five-year deal. Writing on Instagram, he added: "I have conquered everything an athlete can conquer. I have lived unforgettable moments. But a player [me] needs challenges. "And for the second time in my life, I'll contradict my father." Neymar's transfer smashes the previous record set when Paul Pogba returned to Manchester United from Juventus for £89m in August 2016. His £782,000-a-week wages mean PSG's total outlay is £400m. The French side have called a news conference for 12:30 BST on Friday, and Neymar will be introduced to fans at PSG's first game of the season against Amiens at Parc des Princes on Saturday. PSG reached the last eight of the Champions League last season - knocked out by a Neymar-inspired Barcelona - and were beaten to the French title by Monaco. Neymar said he has joined "one of the most ambitious clubs in Europe". "Paris St-Germain's ambition attracted me to the club, along with the passion and the energy this brings," he added. "I feel ready to take the challenge.

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Ans) NLP is exactly what you are doing right now. Or what you do when you hear your mom scream at you. Ans) Understanding a Natural Language (Languages used by humans to exchange information like English, French, German etc). Q) Is it related to Artificial Intelligence and Machine Learning? Ans) Machine Learning (ML) is the'I' (Intelligence) in A.I. ML, at its core, is art of Identifying, Generating, Discriminating and Understanding patterns/relations.