In-context learning (ICL) empowers large pre-trained language models (PLMs) to predict outcomes for unseen inputs without parameter updates. However, the efficacy of ICL heavily relies on the choice of demonstration examples. Randomly selecting from the training set frequently leads to inconsistent performance. Addressing this challenge, this study takes a novel approach by focusing on training data valuation through causal inference. Specifically, we introduce the concept of average marginal effect (AME) to quantify the contribution of individual training samples to ICL performance, encompassing both its generalization and robustness. Drawing inspiration from multiple treatment effects and randomized experiments, we initially sample diverse training subsets to construct prompts and evaluate the ICL performance based on these prompts. Subsequently, we employ Elastic Net regression to collectively estimate the AME values for all training data, considering subset compositions and inference performance. Ultimately, we prioritize samples with the highest values to prompt the inference of the test data. Across various tasks and with seven PLMs ranging in size from 0.8B to 33B, our approach consistently achieves state-of-the-art performance. Particularly, it outperforms Vanilla ICL and the best-performing baseline by an average of 14.1% and 5.2%, respectively. Moreover, prioritizing the most valuable samples for prompting leads to a significant enhancement in performance stability and robustness across various learning scenarios. Impressively, the valuable samples exhibit transferability across diverse PLMs and generalize well to out-of-distribution tasks.

Due to the excellent computing efficiency, learning to hash has acquired broad popularity for Big Data retrieval. Although supervised hashing methods have achieved promising performance recently, they presume that all training samples are appropriately annotated. Unfortunately, label noise is ubiquitous owing to erroneous annotations in real-world applications, which could seriously deteriorate the retrieval performance due to imprecise supervised guidance and severe memorization of noisy data. Here we propose a comprehensive method DIOR to handle the difficulties of learning to hash with label noise. DIOR performs partitions from two complementary levels, namely sample level and parameter level. On the one hand, DIOR divides the dataset into a labeled set with clean samples and an unlabeled set with noisy samples using an ensemble of perturbed views. Then we train the network in a contrastive semi-supervised manner by reconstructing label embeddings for both reliable supervision of clean data and sufficient exploration of noisy data. On the other hand, inspired by recent pruning techniques, DIOR divides the parameters in the hashing network into crucial parameters and non-crucial parameters, and then optimizes them separately to reduce the overfitting of noisy data. Extensive experiments on four popular benchmark datasets demonstrate the effectiveness of DIOR.

Hashing aims to compress raw data into compact binary descriptors, which has drawn increasing interest for efficient large-scale image retrieval. Current deep hashing often employs evaluation protocols where usually query data and training data are from similar distributions. However, more realistic evaluations should take into account a broad spectrum of distribution shifts with varying degrees. Therefore, we study the problem of out-of-distribution generalization in image retrieval, which seeks to learn a retrieval model from a source domain and generalize to unseen target domains. However, this problem is challenging owing to data scarcity in target domains and the potential overfitting of domain-specific patterns. Here, we propose a novel hashing model namedLooking-into-gradients (LOG) for image retrieval under out-of-distribution shifts, which comprehensively explores gradients for both data generation and model optimization. Specifically, to overcome data deficiency in target domains, we formalize the worst-case problem to generate challenging virtue samples via adversarial gradient ascend. Besides, to further enhance model generalization capability, we not only identify non-crucial parameters with minor gradients and values and shrink them to zero, but also modify the inconsistent gradients across domains to prevent learning domain-specific patterns. Extensive experiments on various datasets demonstrate that LOG outperforms state-of-the-art methods by up to 8.54%.

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing model from a label-rich source domain to a label-scarce target domain. Current state-of-the-art approaches generally resolve the problem by integrating pseudo-labeling and domain adaptation techniques into deep hashing paradigms. Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. The core of our DANCE is to perform contrastive learning on hash codes from both instance level and prototype level. To begin, DANCE utilizes label information to guide instance-level hashing contrastive learning in the source domain. To generate unbiased and reliable pseudo-labels for semantic learning in the target domain, we uniformly select samples around each label embedding in the Hamming space. A momentum-update scheme is also utilized to smooth the optimization process. Additionally, we measure the semantic prototype representations in both source and target domains and incorporate them into a domain-aware prototype-level contrastive learning paradigm, which enhances domain alignment in the Hamming space while maximizing the model capacity. Experimental results on a number of well-known domain adaptive retrieval benchmarks validate the effectiveness of our proposed DANCE compared to a variety of competing baselines in different settings.

With the trend of cloud computing, the cloud log service is becoming increasingly important, as it plays a critical role in tasks such as root cause analysis, service monitoring and security audition. To meet these needs, we provide Tencent Cloud Log Service (TencentCLS), a one-stop solution for log collection, storage, analysis and dumping. It currently hosts more than a million tenants, of which the largest ones can generate up to PB-level logs per day. The most important challenge that TencentCLS faces is to support both low-latency and resource-efficient queries on such large quantities of log data. To address that challenge, we propose a novel search engine based upon Lucene. The system features a novel procedure for querying logs within a time range, an indexing technique for the time field, as well as optimized query algorithms dedicated to multiple critical and common query types. As a result, the search engine at TencentCLS gains significant performance improvements against Lucene. It achieves 20x performance increase with standard queries, and 10x performance increase with histogram queries in massive log query scenarios. In addition, TencentCLS also supports storing and querying with microsecond-level time precision, as well as the microsecond-level time order preservation capability.

Distilled BERT models are more suitable for efficient vertical retrieval in online sponsored vertical search with low-latency requirements than BERT due to fewer parameters and faster inference. Unfortunately, most of these models are still far from ideal inference speed. This paper presents a novel and effective method to distill knowledge from BERT into simple fully connected neural networks (FNN). Results of extensive experiments on English and Chinese datasets demonstrate that our method achieves comparable results with existing distilled BERT models while the inference is accelerated by more than ten times. We have successfully applied our method on our online sponsored vertical search engine and get remarkable improvements.

Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as the input to predict the judgment, neglecting critical case life-cycle information in real court setting could threaten the case logic representation quality and prediction correctness. In this paper, we introduce a novel challenging dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case - plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction, achieving significant improvements over strong state-of-the-art baselines. Moreover, the user study conducted with real judges and law school students shows the neural predictions can also be interpretable and easily observed, and thus enhancing the trial efficiency and judgment quality.

Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL and Python, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.