Knowledge graph (KG) has played an important role in enhancing the performance of many intelligent systems. In this paper, we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications. Unlike previous works that build knowledge graph with graph databases, we build the knowledge graph on top of SogouQdb, a distributed search engine developed by Sogou Web Search Department, which can be easily scaled to support petabytes of data. As a supplement to the search engine, we also introduce a series of models to support inference and graph based querying. Currently, the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity links. We also introduce three applications of knowledge graph in Sogou Inc.: entity detection and linking, knowledge based question answering and knowledge based dialog system. These applications have been used in Web search products to help user acquire information more efficiently.

Recently, the evaluation of Large Language Models has emerged as a popular area of research. The three crucial questions for LLM evaluation are ``what, where, and how to evaluate''. However, the existing research mainly focuses on the first two questions, which are basically what tasks to give the LLM during testing and what kind of knowledge it should deal with. As for the third question, which is about what standards to use, the types of evaluators, how to score, and how to rank, there hasn't been much discussion. In this paper, we analyze evaluation methods by comparing various criteria with both manual and automatic evaluation, utilizing onsite, crowd-sourcing, public annotators and GPT-4, with different scoring methods and ranking systems. We propose a new dataset, LLMEval and conduct evaluations on 20 LLMs. A total of 2,186 individuals participated, leading to the generation of 243,337 manual annotations and 57,511 automatic evaluation results. We perform comparisons and analyses of different settings and conduct 10 conclusions that can provide some insights for evaluating LLM in the future. The dataset and the results are publicly available at https://github.com/llmeval. The version with the appendix are publicly available at https://arxiv.org/abs/2312.07398.

Taking several topic words and a math expression as input, the aim of math word problem generation is to generate a problem that can be answered by the given expression and related to these topic words. Considerable progress has been achieved by sequence-to-sequence neural network models in many natural language generation tasks, but these models do not effectively consider the characteristics of the math word problem generation task. They may generate problems that are unrelated to the topic words and expressions, and problems that cannot be solved. In this paper, we propose a new model, MWPGen, for automatically generating math word problems. MWPGen has a topic-expression co-attention mechanism to extract relevant information between topic words and expressions. Further, we fine-tune MWPGen with the solving result of the generated problem as the reward for reinforcement learning. MWPGen shows improved performance in popular automatic evaluation metrics and improves the solvability of generated problems.

Ruotian Ma, Tao Gui, Linyang Li, Qi Zhang, Xuanjing Huang, Yaqian Zhou. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Hashtags are keywords describing a topic or a theme and are usually chosen by microblogging users. Hence, the hashtags can be used to categorize microblog posts. With the fast development of the social network, the task of recommending suitable hashtags has received considerable attention in recent years. Recently, most neural network methods have treated the task as a multi-class classification problem. In fact, users are constantly introducing new hashtags in a highly dynamic way. Treating the task as a multi-class classification problem with a fixed number of target categories does not allow the method to deal with the new hashtags. To address this problem, the task is reinterpreted as a matching problem and a novel co-attention memory network is proposed to represent the multimodal microblogs and hashtags. We utilize a co-attention mechanism to model the multimodal mircroblogs, and utilize the post history to represent the hashtags. Experimental results on a Twitter-based dataset demonstrated that the proposed method can achieve better performance than the current state-of-the-art methods that treat the task as a multi-class classification problem.