SureMap estimates model performance across subpopulations efficiently.
problem Estimating model performance across subpopulations with scarce data.
method Simultaneous Gaussian mean estimation with external data.
result High accuracy in both multi-task and single-task disaggregated evaluations.
TOPNet integrates task-based evaluation into machine learning models.
problem Non-differentiable task-based evaluation criteria in real-world applications.
method Task-Oriented Prediction Network (TOPNet) with learnable surrogate loss function.
result TOPNet significantly outperforms traditional and heuristic models in financial prediction tasks.
DUET optimizes LLM training data mixtures using feedback from unseen tasks.
problem Optimizing LLM performance on unseen tasks without prior data knowledge.
method Interleaves influence function with Bayesian optimization for data selection.
result DUET converges to optimal training data mixture for unseen tasks.
This paper investigates task-level evaluation in few-shot learning models.
problem The reliability of evaluating and tuning models trained for individual tasks in few-shot learning is not well addressed.
method The paper measures accuracy of performance estimators, considers model selection strategies, and examines the reasons for evaluator failure.
result Cross-validation with a low number of folds is best for estimating model performance, while large number of folds is better for model selection.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
HEAR benchmark evaluates audio representations for diverse tasks.
problem Developing a general-purpose audio representation for various tasks.
method Evaluated 29 models across 19 tasks using 16 datasets.
result No single audio representation performs holistically.
New model improves diversity in question generation over text.
problem Inadequate evaluation metrics for diverse questions.
method Proposed a variational encoder-decoder model and evaluation scheme.
result Improved diversity in generated questions without loss of quality.
Hierarchical framework for model evaluation on leaderboards
problem Uncertainty and variability in model performance across tasks
method Hierarchical framework with task-level and leaderboard-level rank prediction intervals
result Statistically valid and informative model rank intervals
Progress in machine learning is measured by careful evaluation on problems of outstanding common interest. However, the proliferation of benchmark suites and environments, adversarial attacks, and other complications has diluted the basic evaluation model by overwhelming researchers with choices. Deliberate or accident…
QUACKIE creates a new benchmark for NLP interpretability.
problem Evaluating NLP interpretability methods is challenging due to biased ground truths.
method Formulated a custom classification task from question-answering datasets, generating unbiased ground truths.
result Demonstrated the effectiveness of current interpretability methods on the new benchmark.
Study evaluates financial misstatement detection methods, highlighting evaluation process impact.
problem Detecting financial reports with high misstatement risk.
method Proposes a new, realistic evaluation framework focusing on misstatement rarity, time dimension, and detection latency.
result Evaluation process significantly impacts system performance, revealing model and feature type effectiveness.
Proposes a method to evaluate meta-learning performance based on task similarity.
problem Meta-learning performance evaluation ignores task similarity, leading to biased results.
method Generative approach using Latent Dirichlet Allocation to analyze task similarity.
result The proposed method provides an insightful evaluation of meta-learning algorithms, matching common intuition.
Learning new tasks with few samples using related task evaluations.
problem Learning a new task with limited data and related task evaluations.
method Modeling task relatedness through weak monotonicity and leveraging it in transfer learning and model selection aggregation.
result Pruning the model class based on monotonicity and hedging on the task frontier.
Automated EEG analysis gauges mental workload in task evaluation.
problem Evaluating mental workload in user tasks, especially complex ones.
method Experimental setup, EEG data analysis, feature extraction, machine learning.
result Machine learning outperforms traditional methods in mental workload estimation.
This work evaluates task-agnostic exploration methods for fixed-batch learning.
problem Expensive real-world experience for robotics tasks.
method Fixed datasets for arbitrary task learning.
result Improved offline learning for robotics tasks.
GraphBench creates a unified benchmark for graph learning tasks.
problem Fragmented benchmarking practices and inconsistent evaluation protocols in graph learning.
method Developed a comprehensive benchmark suite with standardized evaluation protocols.
result Established principled baselines for future research in graph learning.
New method improves consistency of reinforcement learning performance evaluations.
problem Inconsistent performance results in reinforcement learning due to flawed evaluation metrics.
method Proposes a new comprehensive evaluation methodology for reinforcement learning algorithms.
result Demonstrates improved reliability of performance measurements for reinforcement learning algorithms.
This work benchmarks MARL algorithms in cooperative tasks.
problem Lack of evaluation tasks and criteria for comparing MARL algorithms.
method Systematic evaluation of three MARL algorithm classes in diverse cooperative tasks.
result Insights into the effectiveness of different learning approaches.
VLM judges rank well but score poorly; task difficulty and annotation quality affect interval width.
problem VLMs as judges lack reliability indicators in multimodal evaluations.
method Conformal prediction using score-token log-probabilities.
result Evaluation uncertainty is task-dependent, affecting interval width and reliability.
Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments. Token-level assignments are important for downstream tasks such as classification. Even recent models, which aim to improv…
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most of these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing…
AI benchmarks evaluate football team performance using generative models.
problem Evaluating human performance in complex interactive tasks is error-prone and unreliable.
method Trained Conditional VRNN Model on player and ball tracking data to imitate and predict team interactions.
result Trained model as a useful benchmark for evaluating team performance in football.
GSR optimizes tasks in scientific workflows, improving performance across diverse applications.
problem Uncertainty in task selection and evaluation in scientific workflow optimization.
method Generate-Select-Refine (GSR) framework that alternates between task generation and optimization.
result GSR outperforms existing LLM-based optimizers in various scientific applications.
Bayesian framework evaluates predictors of subjective visual tasks.
problem Evaluating uncertainty in machine learning predictors for tasks with subjective annotations.
method Bayesian framework to estimate epistemic uncertainty from human labels.
result Framework successfully applied to four image classification tasks.
UCFE benchmarks LLMs in financial tasks with human feedback.
problem Evaluating LLMs' financial task performance and user satisfaction.
method Hybrid approach combining human expert evaluations and dynamic interactions.
result Significant alignment between benchmark scores and human preferences (Pearson correlation coefficient of 0.78).
The paper proposes a method to evaluate superhuman models by checking for logical inconsistencies.
problem Evaluating superhuman models when ground truth is hard to obtain.
method A framework using consistency checks to identify logical inconsistencies in model decisions.
result Logical inconsistencies can be discovered in superhuman model decisions across various tasks.
One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work …
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empiri…
Meta-World benchmark tests meta-reinforcement learning on 50 robotic tasks.
problem Current meta-reinforcement learning benchmarks are too narrow, limiting generalization.
method Proposes a new benchmark with 50 robotic manipulation tasks.
result State-of-the-art algorithms struggle with multiple tasks simultaneously.
State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in appl…
Paper tackles counterfactual sentence detection and evaluation.
problem Detect and evaluate counterfactual sentences in natural language.
method Used a BERT base model for classification and a hybrid BERT Multi-Layer Perceptron for sequence identification. Introduced cascaded linear inputs to improve performance.
result Achieved an F1 score of 85.00% in Task 1 and 83.90% in Task 2.
In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms. The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The chara…
RETINA Benchmark evaluates Bayesian deep learning on diabetic retinopathy detection.
problem Reliable uncertainty quantification for deep learning models in medical applications.
method Design and evaluation of a real-world diabetic retinopathy dataset and tasks.
result Benchmarking of Bayesian deep learning methods on diabetic retinopathy detection tasks.
Memory-augmented neural networks (MANNs) have been shown to outperform other recurrent neural network architectures on a series of artificial sequence learning tasks, yet they have had limited application to real-world tasks. We evaluate direct application of Neural Turing Machines (NTM) and Differentiable Neural Compu…
Unified evaluation framework for sampling methods.
problem Lack of a standardized evaluation framework for sampling methods.
method Introduces a benchmark suite and performance criteria for sampling methods.
result Insights into strengths and weaknesses of existing sampling methods.
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
InvestorBench benchmarks LLM agents in financial tasks.
problem Lack of a comprehensive benchmark for LLM-based financial agents.
method Developed a benchmark with diverse financial tasks and datasets.
result Evaluated LLM agents' performance across various financial products and market environments.
The paper ranks experts based on task performance with noisy evaluations.
problem Ranking experts based on their performance across multiple tasks with noisy evaluations.
method Develops adaptive strategies for ranking experts with a bound on the number of queries.
result Proves strategies allowing to recover the correct ranking of experts with high probability.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.
Open-FinLLMs tackle financial tasks with multimodal capabilities.
problem Financial LLMs lack multimodal capabilities and real-world applicability.
method Developed Open-FinLLMs, an open-source multimodal financial LLM suite.
result Open-FinLLMs outperform advanced financial and general LLMs in diverse tasks.
This paper reviews causal inference methods for time series data.
problem Estimating treatment effects and identifying causal relations from time series data.
method Comprehensive review of approaches for treatment effect estimation and causal discovery.
result Provides a list of evaluation metrics and datasets for time series causal inference.
New benchmark evaluates BDL methods in medical retinopathy diagnosis.
problem Evaluate robustness and scalability of BDL methods in medical applications.
method Developed a new benchmark with real-world diabetic retinopathy tasks.
result Some BDL techniques overfit uncertainty to datasets, underperforming on new benchmark.
We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine tra…
Owing to the advancement of deep learning, artificial systems are now rival to humans in several pattern recognition tasks, such as visual recognition of object categories. However, this is only the case with the tasks for which correct answers exist independent of human perception. There is another type of tasks for w…
FINCH dataset enables financial Text-to-SQL tasks, improving model evaluation.
problem Lack of a large-scale financial dataset for Text-to-SQL tasks.
method Curated financial dataset (FINCH), benchmarking reasoning and language models.
result Proposed FINCH Score for more accurate financial model evaluation.
MUMBO optimizes multiple tasks efficiently, even with low-cost related functions.
problem Efficiently optimizing multiple related functions with low-cost evaluations.
method Derives a novel multi-task version of entropy search.
result Robust performance with low computational overhead across various optimization challenges.
Large language models correlate in errors, even with different architectures and providers.
problem Lack of empirical evidence on whether different large language models differ meaningfully.
method Empirical evaluation of over 350 large language models using two leaderboards and a resume-screening task.
result Large language models have substantial correlation in errors, even with distinct architectures and providers.
This paper evaluates various representations for robotics tasks, improving performance in lifting, stacking, and pushing.
problem Improving data-efficiency in reinforcement learning for robotics with limited data.
method Systematic evaluation of common representations in three robotics tasks: lifting, stacking, and pushing.
result Some representations can perform as well as simulator states as agent inputs, challenging common intuitions.