This paper addresses the challenge of unsupervised plain graph alignment, specifically in scenarios where auxiliary information, such as node attributes, is unavailable. Existing alignment algorithms primarily fall into two categories: spectral methods and representation learning-based methods. Spectral methods typically leverage alignment consistency principles, employing heuristic strategies to iteratively infer the alignment matrix. In contrast, representation learning methods focus on encoding the geometric structural features of nodes to generate node representations, thereby transforming the node matching task into a similarity computation based on these representations. While both approaches demonstrate robust performance in the graph alignment domain, their time complexity poses significant concerns. To mitigate this issue, we propose a novel, efficient algorithm grounded in seed-oriented subgraph matching. Our method begins by extracting a limited number of reliable pseudo alignment seeds derived from graph geometric features. Subsequently, we extract the corresponding K-hop seed-oriented subgraphs, allowing us to reformulate the graph alignment problem into a series of subgraph matching tasks. The final alignment matrix is then constructed by aggregating the results of these subgraph matches. Experimental evaluations conducted on public datasets reveal that our method not only improves efficiency but also outperforms current state-of-the-art techniques in terms of accuracy.

Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions. The quality of instruction pairs used for tuning greatly affects the performance of LLMs. However, the manual creation of high-quality instruction datasets is costly, leading to the adoption of automatic generation of instruction pairs by LLMs as a popular alternative. To ensure the high quality of LLM-generated instruction datasets, several approaches have been proposed. Nevertheless, existing methods either compromise dataset integrity by filtering a large proportion of samples, or are unsuitable for industrial applications. In this paper, instead of discarding low-quality samples, we propose CoachLM, a novel approach to enhance the quality of instruction datasets through automatic revisions on samples in the dataset. CoachLM is trained from the samples revised by human experts and significantly increases the proportion of high-quality samples in the dataset from 17.7% to 78.9%. The effectiveness of CoachLM is further assessed on various real-world instruction test sets. The results show that CoachLM improves the instruction-following capabilities of the instruction-tuned LLM by an average of 29.9%, which even surpasses larger LLMs with nearly twice the number of parameters. Furthermore, CoachLM is successfully deployed in a data management system for LLMs at Huawei, resulting in an efficiency improvement of up to 20% in the cleaning of 40k real-world instruction pairs. We release various assets of CoachLM, including the training data, code and test set11https://github.com/lunyiliu/CoachLM.

Entity correction is crucial in Automatic Speech TABLE I Recognition (ASR), since erroneous entities seriously affect our understanding of ASR results. In this paper, in order to correct entity errors, we propose a knowledge prompt approach for Whisper (a recent ASR model trained with a corpus containing 680k hours of labeled speech recorded in various conditions). For a given audio, our approach consists of three steps: (1) obtaining its ASR result by Whisper; (2) fuzzy matching the ASR result with a knowledge base to obtain candidate entities; (3) using the candidate entities as a prompt to obtain the final ASR result by Whisper again. We conduct experiments on the test dataset of open-source Chinese speech corpus AISHELLNER. Experimental results show that our approach not only significantly improves the entity recall rate in ASR results (from 70.97% to 84.82%), but also reduces the overall Character Error Rate (CER).

As Neural Machine Translation (NMT) heavily relies on training data, finding an effective method to help NMT make better use of limited data is of great significance. In this paper, with the motivation of the famous Google’s PageRank algorithm, we propose a novel unsupervised method EntityRank for mining bilingual named entity pairs from parallel corpora, which involves three critical components (Generator, Scorer and Filter). To apply the pairs mined by EntityRank to NMT, we design a data augmentation strategy for the state-of-the-art (SOTA) model Transformer. From the experimental results on the CCMT20 English-Chinese and WMT14 English-German news parallel corpora, it can be seen that the unsupervised method EntityRank could obtain relatively high quality bilingual named entity pairs; and with the designed data augmentation strategy, the mined pairs could not only significantly improve the translation quality of their covered data, but also benefit the translation quality of the overall data.