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Research, evidence gathering, literature, reports, investigation, and synthesis
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- Baskethar EvalEvaluates multimodal human activity recognition capabilities in basketball training scenarios by classifying complex dynamic movements from synchronized physiological, inertial, and video sensor data. Use when the user wants to benchmark on BasketHAR, or asks about evaluating this task. Reports F1-score.Votes: 0GitHub stars: 3
- BartscoreBARTScore evaluates the quality of generated text by treating evaluation as a conditional text generation task. It measures the likelihood of a hypothesis given a source text, or a reference given a hypothesis, using pre-trained sequence-to-sequence models. This approach enables unsupervised, multi-perspective assessment of fluency, factuality, and informativeness without relying on human annotations or simple n-gram overlap. Use when the user has predictions and gold and needs to compute Spe...Votes: 0GitHub stars: 3
- Bart EvalEvaluates a denoising sequence-to-sequence pre-trained model across discriminative comprehension, abstractive text generation, dialogue response, and machine translation tasks to measure cross-task generalization and generation quality. Use when the user wants to benchmark on SQuAD 1.1, SQuAD 2.0, GLUE, CNN/DailyMail, XSum, ConvAI2, ELI5, WMT'16 RO-EN, or asks about evaluating this task. Reports ROUGE.Votes: 0GitHub stars: 3
- Bars Recommender EvalEvaluates the reproducibility and standardization of evaluation protocols in recommender systems. It probes both candidate item matching (ranking) and click-through rate (CTR) prediction tasks using standardized data splits, hyperparameter configurations, and common industry metrics to ensure fair and comparable model performance. Use when the user wants to benchmark on Criteo, MovieLens, or asks about evaluating this task. Reports NDCG@K, AUC.Votes: 0GitHub stars: 3
- Bar Exam Qa EvalEvaluates a model's ability to retrieve relevant legal passages and answer legal questions that require multi-hop or analogical reasoning, characterized by low lexical overlap between queries and documents. Use when the user wants to benchmark on Bar Exam QA, or asks about evaluating this task. Reports Recall@10.Votes: 0GitHub stars: 3
- Banglabook Sentiment EvalEvaluates the ability of models to classify Bangla book reviews into three sentiment categories (Positive, Neutral, Negative). It probes product-specific sentiment analysis in a low-resource language, testing both contextual understanding and robustness to class imbalance and lexical overlap. Use when the user wants to benchmark on BANGLABOOK, or asks about evaluating this task. Reports weighted average F1-score.Votes: 0GitHub stars: 3
- Banglaberse EvalProbes multilingual vision-language models' ability to understand and reason about Bengali cultural concepts across regional dialects and historically linked languages. It measures how well models maintain cultural grounding when faced with linguistic variation, testing both visual captioning and structured question-answering capabilities. Use when the user wants to benchmark on BanglaVerse, or asks about evaluating this task. Reports accuracy (%).Votes: 0GitHub stars: 3
- Bangla Sentiment EvalEvaluates the capability of NLP models to classify sentiment in Bangla text. It compares classical machine learning, CNN, FastText, and transformer-based architectures to determine which model family performs best on low-resource Bangla sentiment tasks. Use when the user wants to benchmark on Multiple publicly available Bangla sentiment datasets, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Bangla Math Olympiad EvalEvaluates large language models' ability to solve mathematical Olympiad problems in Bangla and English. It probes multilingual reasoning, step-by-step problem solving, and the impact of retrieval-augmented generation and fine-tuning on low-resource language math tasks. Use when the user wants to benchmark on BDMO dataset, Test dataset, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Bangla Key2text EvalEvaluates a model's ability to generate coherent, faithful Bangla text conditioned on a set of extracted keywords, testing sequence-to-sequence generation capabilities in a low-resource language setting. Use when the user wants to benchmark on Bangla Key2Text, or asks about evaluating this task. Reports generation_quality.Votes: 0GitHub stars: 3
- Balsam EvalEvaluates Arabic large language models across 14 diverse NLP categories, including creative writing, question answering, reading comprehension, logic, and machine translation. It probes the models' ability to handle complex Arabic morphology, long-form generation, and task-specific reasoning. Use when the user wants to benchmark on BALSAM, or asks about evaluating this task. Reports LLM as a judge.Votes: 0GitHub stars: 3
- Balsa Audio EvalEvaluates audio-language alignment, reasoning, and instruction-following capabilities of audio-aware large language models. It probes the model's ability to answer audio-based questions, perform semantic reasoning, detect hallucinations, and follow complex multimodal instructions. Use when the user wants to benchmark on ClothoAQA, Synonym-Hypernym Test, MMAU, MMAR, SAKURA, Audio Hallucination Benchmark, Instruction-Following Benchmark, or asks about evaluating this task. Reports accuracy, wei...Votes: 0GitHub stars: 3
- Badrobot EvalEvaluates the safety and alignment of embodied LLMs by measuring their susceptibility to voice-based and text-based adversarial prompts that induce harmful physical actions, privacy violations, or fraud. It probes cascading jailbreaks, safety misalignment between language and action, and gaps in physical world knowledge. Use when the user wants to benchmark on BadRobot Physical Action Benchmark, or asks about evaluating this task. Reports MSR (Manipulate Success Rate).Votes: 0GitHub stars: 3
- Backx Attribution EvalThis benchmark evaluates the fidelity and reliability of explainable AI (XAI) attribution methods in identifying backdoor triggers versus natural image features. It tests whether attribution techniques can consistently highlight injected trigger patterns across different visibility levels and attack types, while remaining invariant to clean input distributions. Use when the user wants to benchmark on CIFAR-10, GTSRB, ImageNet 2012, or asks about evaluating this task. Reports trigger recall.Votes: 0GitHub stars: 3
- Backdoormbti EvalThis benchmark evaluates the robustness and effectiveness of multimodal backdoor attacks and defense mechanisms across image, text, and audio modalities. It specifically probes how well defenses maintain clean accuracy while suppressing attack success rates under varying noise conditions and label corruption. Use when the user wants to benchmark on CIFAR-10, SST-2, SpeechCommands, or asks about evaluating this task. Reports ASR, accuracy.Votes: 0GitHub stars: 3
- Backdoor Detection Purification EvalEvaluates language models' vulnerability to backdoor attacks and the effectiveness of detection and purification defenses. It probes whether a model can correctly classify clean text while resisting trigger-induced misclassifications, and whether a defense can identify poisoned samples without degrading benign task performance. Use when the user wants to benchmark on SST-2, YELP, AG’s News, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Backbone Optimizer Coupling EvalProbes the interdependence between vision backbone architectures and optimization algorithms by measuring how different backbones perform when paired with various optimizers across classification and detection tasks. It evaluates whether architectural design dictates optimal optimizer choice and how this coupling affects transfer learning and hyperparameter robustness. Use when the user wants to benchmark on CIFAR-100, ImageNet-1K, COCO, or asks about evaluating this task. Reports Top-1 accur...Votes: 0GitHub stars: 3
- Backbone Generation EvalThis benchmark evaluates the designability, structural diversity, and novelty of generated protein backbones across varying lengths. It measures how well diffusion models can produce foldable and structurally distinct protein scaffolds. Use when the user wants to benchmark on Protein Backbone Generation Benchmark, or asks about evaluating this task. Reports scRMSD.Votes: 0GitHub stars: 3
- Backbone Fine Tuning EvalEvaluates the fine-tuning performance of lightweight, pre-trained CNN and attention-based backbones across diverse image classification domains, including natural images, remote sensing, medical histopathology, and plant imaging. It probes how well different architectures generalize under data-scarce conditions and whether ImageNet pre-training accuracy correlates with downstream task performance. Use when the user wants to benchmark on CIFAR-10, CIFAR-100, Tiny ImageNet, Stanford Dogs, Flowe...Votes: 0GitHub stars: 3
- Backbench EvalThis benchmark probes an agent's ability to recover from harmful states in real-world computer use environments by backtracking or remediating to a safe operational state. It evaluates how well agents align with human preferences during recovery under varying resource constraints (step limits). Use when the user wants to benchmark on BackBench, or asks about evaluating this task. Reports Bradley-Terry rating.Votes: 0GitHub stars: 3
- Back Translation Wake Sleep EvalEvaluates neural machine translation models on English-German, German-English, English-Latvian, and Latvian-English translation tasks. It probes the effectiveness of iterative back-translation (wake-sleep extension) compared to standard back-translation and baseline MLE training across supervised and semi-supervised domain adaptation scenarios. Use when the user wants to benchmark on WMT 2017, TED (IWSLT 2014), or asks about evaluating this task. Reports BLEU (SACREBLEU v1.2.3).Votes: 0GitHub stars: 3
- Babyvision EvalEvaluates fundamental visual reasoning capabilities in multimodal large language models independent of linguistic priors. It probes early-vision abilities such as visual tracking, spatial perception, fine-grained discrimination, and visual pattern recognition through image-based tasks. Use when the user wants to benchmark on BabyVision, or asks about evaluating this task. Reports Avg@3.Votes: 0GitHub stars: 3
- Babyslm EvalEvaluates the lexical and syntactic competence of self-supervised spoken language models using child-centered, developmentally plausible speech data. It probes whether models can acquire language-like representations from ecologically valid, in-the-wild audio recordings compared to clean audiobooks or text-based inputs. Use when the user wants to benchmark on BabySLM, or asks about evaluating this task. Reports lexical accuracy.Votes: 0GitHub stars: 3
- Baboonland EvalThis benchmark evaluates computer vision models on three core tasks using drone footage of wild baboons: object detection, multi-object tracking, and fine-grained behavioral recognition. It probes a model's ability to handle extreme scale variation, heavy occlusion, and temporal context in natural, uncontrolled wildlife environments. Use when the user wants to benchmark on BaboonLand, or asks about evaluating this task. Reports Top-1 accuracy.Votes: 0GitHub stars: 3