Attributed text generation aims to enhance the reliability of content generated from large language models by providing citations for each claim, which thereby enables users to easily verify the correctness of the responses.However, the scarcity of high-quality training samples presents a significant challenge in aligning large language models to generate texts with citations, revealing considerable room for improvement in existing attribution systems.Besides, existing approaches of aligning large language models to follow user instructions can lead to an undue emphasis on irrelevant documents, which in turn reduces the quality of responses.To address the above problems, we propose Automatic Alignment Framework for Attributed Text Generation (A 3 ), a novel framework designed to automatically generate highquality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation.With the help of A 3 , Mistral-7B can achieve a citation recall of 84.4 and a precision of 87.0 precision on ASQA, which notably surpasses GPT-4's citation recall of 73.0 and precision of 76.5. 1

Large Language Models (LLMs) hold considerable promise for artificial general intelligence, given their intrinsic abilities to accomplish a wide range of open-domain tasks either independently or in tandem with specialized expert models. However, despite these capabilities, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios. To this end, in this work, we introduce a novel task, the Realistic Chinese Spell Checking (RCSC), to evaluate the effectiveness of existing methods comprehensively. In contrast to existing works that solely address Chinese character misspellings or pinyin conversions, our task aims to convert the realistic Chinese text into the corresponding correct text. The realistic Chinese text may potentially contain both Chinese misspellings and pinyin conversions. We first present the Realistic Chinese Spell Checking Benchmark (RCSCB), which consists of two subsets and contains a total of 581,657 samples. Then, we benchmark the performance of various baselines and find that all the existing methods, including instruction-based LLMs, achieve unsatisfactory results on RCSCB. To further improve the performance on RCSCB, we propose Pinyin-Enhanced Spell Checker (PESC), which is specifically designed to address pinyin-related misspellings. Experimental results demonstrate that PESC can achieve state-of-the-art performance on RCSCB. Despite the progress made, the current state-of-the-art performance is still far from satisfactory. We expect further progress on this crucial and challenging task.

Text matching systems have become a fundamental service in most Searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multi-intent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result. Extensive experiments on three real-world datasets from different matching scenarios show that MIM significantly outperforms state-of-the-art matching baselines. MIM is also tested by online A/B test, which brings significant improvements over three business metrics in query rewriting and query-item relevance tasks compared with the online baseline in Alipay App.

Misspellings in search engine queries may prevent search engines from returning accurate results. For Chinese mobile search engines, due to the different input methods (e.g., hand-written and T9 input methods), more types of misspellings exist, making this problem more challenging. As an essential module of search engines, Chinese Spelling Check~(CSC) models aim to detect and correct misspelled Chinese characters from user-issued queries. Despite the great value of CSC to the search engine, there is no CSC benchmark collected from real-world search engine queries. To fill this blank, we construct and release the Alipay Search Engine Query (AlipaySEQ) spelling check dataset. To the best of our knowledge, AlipaySEQ is the first Chinese Spelling Check dataset collected from the real-world scenario of Chinese mobile search engines. It consists of 15,522 high-quality human annotated and 1,175,151 automatically generated samples. To demonstrate the unique challenges of AlipaySEQ in the era of Large Language Models~(LLMs), we conduct a thorough study to analyze the difference between AlipaySEQ and existing SIGHAN benchmarks and compare the performance of various baselines, including existing task-specific methods and LLMs. We observe that all baselines fail to perform satisfactorily due to the over-correction problem. Especially, LLMs exhibit below-par performance on AlipaySEQ, which is rather surprising. Therefore, to alleviate the over-correction problem, we introduce a model-agnostic CSC Self-Refine Framework (SRF) to construct a strong baseline. Comprehensive experiments demonstrate that our proposed SRF, though more effective against existing models on both the AlipaySEQ and SIGHAN15, is still far from achieving satisfactory performance on our real-world dataset. With the newly collected real-world dataset and strong baseline, we hope more progress can be achieved on such a challenging and valuable task.

Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.