The task of Text-to-SQL enables anyone to retrieve information from SQL databases using natural language. Despite several challenges, recent models have made remarkable advancements in this task using large language models (LLMs). Interestingly, we find that LLM-based models without fine-tuning exhibit distinct natures compared to their fine-tuned counterparts, leading to inadequacies in current evaluation metrics to accurately convey their performance. Thus, we analyze the two primary metrics, Test Suite Execution Accuracy (EXE) and Exact Set Matching Accuracy (ESM), to examine their robustness for this task and address shortcomings. We compare the performance of 9 LLM-based models using EXE, the original ESM, and our improved ESM (called ESM+). Our results show that EXE and ESM have high false positive and negative rates of 11.3% and 13.9%, while ESM+ gives those of 0.1% and 2.6% respectively, providing a significantly more stable evaluation. We release the ESM+ script as open-source for the community to contribute, while enjoying a more reliable assessment of Text-to-SQL.

In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of objects across diverse environments. To this end, we proposed Open Vocabulary Mobile Manipulation as a key benchmark task for robotics: finding any object in a novel environment and placing it on any receptacle surface within that environment. We organized a NeurIPS 2023 competition featuring both simulation and real-world components to evaluate solutions to this task. Our baselines on the most challenging version of this task, using real perception in simulation, achieved only an 0.8% success rate; by the end of the competition, the best participants achieved an 10.8\% success rate, a 13x improvement. We observed that the most successful teams employed a variety of methods, yet two common threads emerged among the best solutions: enhancing error detection and recovery, and improving the integration of perception with decision-making processes. In this paper, we detail the results and methodologies used, both in simulation and real-world settings. We discuss the lessons learned and their implications for future research. Additionally, we compare performance in real and simulated environments, emphasizing the necessity for robust generalization to novel settings.

We explore multiple-instance verification, a problem setting where a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. We show that naive adaptations of attention-based multiple instance learning (MIL) methods and standard verification methods like Siamese neural networks are unsuitable for this setting: directly combining state-of-the-art (SOTA) MIL methods and Siamese networks is shown to be no better, and sometimes significantly worse, than a simple baseline model. Postulating that this may be caused by the failure of the representation of the target bag to incorporate the query instance, we introduce a new pooling approach named ``cross-attention pooling'' (CAP). Under the CAP framework, we propose two novel attention functions to address the challenge of distinguishing between highly similar instances in a target bag. Through empirical studies on three different verification tasks, we demonstrate that CAP outperforms adaptations of SOTA MIL methods and the baseline by substantial margins, in terms of both classification accuracy and quality of the explanations provided for the classifications. Ablation studies confirm the superior ability of the new attention functions to identify key instances.

Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have proposed various measures to evaluate model performances. However, both theoretical analysis and empirical observations show that a model might perform inconsistently on different measures. To bridge this gap, this paper proposes a novel measure named Top-K Pairwise Ranking (TKPR), and a series of analyses show that TKPR is compatible with existing ranking-based measures. In light of this, we further establish an empirical surrogate risk minimization framework for TKPR. On one hand, the proposed framework enjoys convex surrogate losses with the theoretical support of Fisher consistency. On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction. Finally, empirical results on benchmark datasets validate the effectiveness of the proposed framework.

Existing research on learning with noisy labels predominantly focuses on synthetic label noise. Although synthetic noise possesses well-defined structural properties, it often fails to accurately replicate real-world noise patterns. In recent years, there has been a concerted effort to construct generalizable and controllable instance-dependent noise datasets for image classification, significantly advancing the development of noise-robust learning in this area. However, studies on noisy label learning for text classification remain scarce. To better understand label noise in real-world text classification settings, we constructed the benchmark dataset NoisyAG-News through manual annotation. Initially, we analyzed the annotated data to gather observations about real-world noise. We qualitatively and quantitatively demonstrated that real-world noisy labels adhere to instance-dependent patterns. Subsequently, we conducted comprehensive learning experiments on NoisyAG-News and its corresponding synthetic noise datasets using pre-trained language models and noise-handling techniques. Our findings reveal that while pre-trained models are resilient to synthetic noise, they struggle against instance-dependent noise, with samples of varying confusion levels showing inconsistent performance during training and testing. These real-world noise patterns pose new, significant challenges, prompting a reevaluation of noisy label handling methods. We hope that NoisyAG-News will facilitate the development and evaluation of future solutions for learning with noisy labels.

This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Time (PIAT) values and other derived metrics, to infer the behavior of non-observable (NO) segments. Through a comprehensive evaluation, we identify an optimal O/NO split threshold of 10 observed delay samples, balancing prediction accuracy and resource utilization. Evaluating models including Logistic Regression, XGBoost, and Multi-Layer Perceptron, we find XGBoost outperforms others, achieving an F1-score of 0.74, balanced accuracy of 0.84, and AUROC of 0.97. Our findings highlight the effectiveness of incorporating comprehensive early flow features and the potential of our method to offer a practical solution for monitoring network traffic in resource-constrained environments. This approach ensures enhanced user experience and network performance by preemptively addressing potential SD, providing the basis for a robust framework for maintaining high-quality network services.

One of the primary catalysts fueling advances in artificial intelligence (AI) and machine learning (ML) is the availability of massive, curated datasets. A commonly used technique to curate such massive datasets is crowdsourcing, where data are dispatched to multiple annotators. The annotator-produced labels are then fused to serve downstream learning and inference tasks. This annotation process often creates noisy labels due to various reasons, such as the limited expertise, or unreliability of annotators, among others. Therefore, a core objective in crowdsourcing is to develop methods that effectively mitigate the negative impact of such label noise on learning tasks. This feature article introduces advances in learning from noisy crowdsourced labels. The focus is on key crowdsourcing models and their methodological treatments, from classical statistical models to recent deep learning-based approaches, emphasizing analytical insights and algorithmic developments. In particular, this article reviews the connections between signal processing (SP) theory and methods, such as identifiability of tensor and nonnegative matrix factorization, and novel, principled solutions of longstanding challenges in crowdsourcing -- showing how SP perspectives drive the advancements of this field. Furthermore, this article touches upon emerging topics that are critical for developing cutting-edge AI/ML systems, such as crowdsourcing in reinforcement learning with human feedback (RLHF) and direct preference optimization (DPO) that are key techniques for fine-tuning large language models (LLMs).

Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessment using Likert-scale scores. This limits deeper model analysis, e.g., we can only assign one hallucination score at the summary level, while at the sentence level, we can count sentences containing hallucinations. To remedy those limitations, we propose FineSurE, a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs). It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment. We compare various open-source and proprietary LLMs as backbones for FineSurE. In addition, we conduct extensive benchmarking of FineSurE against SOTA methods including NLI-, QA-, and LLM-based methods, showing improved performance especially on the completeness and conciseness dimensions. The code is available at https://github.com/DISL-Lab/FineSurE-ACL24.

Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATEGAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATEGAN and PrivBayes. We also conducted a privacy-utility trade-off analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions.

Sepsis poses a major global health threat, accounting for millions of deaths annually and significant economic costs. Accurate predictions of mortality risk in sepsis patients facilitate the efficient allocation of medical resources, thereby enhancing patient survival and quality of life. Through precise risk assessments, healthcare facilities can effectively distribute intensive care beds, medical equipment, and staff, ensuring high-risk patients receive timely and appropriate care. Early identification and intervention significantly decrease mortality rates and improve patient outcomes. Current methods typically utilize only one type of data--either constant, temporal, or ICD codes. This study introduces the Time-Constant KAN Integrated Network(TCKIN), an innovative model that enhances the accuracy of sepsis mortality risk predictions by integrating both temporal and constant data from electronic health records and ICD codes. Validated against the MIMIC-III and MIMIC-IV datasets, TCKIN surpasses existing machine learning and deep learning methods in accuracy, sensitivity, and specificity. Notably, TCKIN achieved AUCs of 87.76% and 88.07%, demonstrating superior capability in identifying high-risk patients. Additionally, TCKIN effectively combats the prevalent issue of data imbalance in clinical settings, improving the detection of patients at elevated risk of mortality and facilitating timely interventions. These results confirm the model's effectiveness and its potential to transform patient management and treatment optimization in clinical practice. With this advanced risk assessment tool, healthcare providers can devise more tailored treatment plans, optimize resource utilization, and ultimately enhance survival rates and quality of life for sepsis patients.