LR-bench

Tasks:

Text Classification

Languages:

Tags:

Dataset card Data Studio Files Files and versions

xet

Community

Dataset Preview

Duplicate

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<author_id: string, papers: list<item: struct<title: string, abstract: string>>>
to
{'paper_title': Value('string'), 'paper_id': Value('string'), 'abstract': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<author_id: string, papers: list<item: struct<title: string, abstract: string>>>
              to
              {'paper_title': Value('string'), 'paper_id': Value('string'), 'abstract': Value('string')}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

anchor dict	positive dict	negative dict	type string
{ "paper_title": "A New Image Quality Database for Multiple Industrial Processes", "paper_id": "paper_100000", "abstract": "Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece ins...	{ "score": 5, "author_id": "author_695265", "papers": [ { "title": "LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images", "abstract": "Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variab...	{ "score": 3, "author_id": "author_592279", "papers": [ { "title": "CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy", "abstract": "(D) Ground truth (A) Input raw image (B) C5 results using different add. images (C) Our result Error = 0.32°Error = 8.14°Error...	paper_centric
{ "paper_title": "A New Image Quality Database for Multiple Industrial Processes", "paper_id": "paper_100000", "abstract": "Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece ins...	{ "score": 5, "author_id": "author_695265", "papers": [ { "title": "LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images", "abstract": "Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variab...	{ "score": 3, "author_id": "author_656045", "papers": [ { "title": "AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception", "abstract": "The image may be a machine-generated image depicting a birthday party scene. There are many characters in the picture, giving people...	paper_centric
{ "paper_title": "A Separable Self-attention Inspired by the State Space Model for Computer Vision", "paper_id": "paper_100001", "abstract": "Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods ...	{ "score": 4, "author_id": "author_445191", "papers": [ { "title": "NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results", "abstract": "This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this chal...	{ "score": 2, "author_id": "author_610649", "papers": [ { "title": "Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects https://github.com/Shiy-Li/Awesome-Graph-augmented-LLM-Agent", "abstract": "Autonomous agents based on large language models (LLMs) have demonstrat...	paper_centric
{ "paper_title": "ADAPTING PROMPTORE FOR MODERN HISTORY: INFORMATION EXTRACTION FROM HISPANIC MONARCHY DOCUMENTS OF THE XVI TH CENTURY A PREPRINT", "paper_id": "paper_100002", "abstract": "Semantic relations among entities are a widely accepted method for relation extraction. PromptORE (Prompt-based Open Relation...	{ "score": 3, "author_id": "author_400040", "papers": [ { "title": "ECAFormer: Low-light Image Enhancement using Dual Cross Attention", "abstract": "Low-light image enhancement (LLIE) aims to improve the perceptibility and interpretability of images captured in poorly illuminated environments. Exi...	{ "score": 1, "author_id": "author_264483", "papers": [ { "title": "Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection", "abstract": "Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as expli...	paper_centric
{ "paper_title": "AGENTOPS: ENABLING OBSERVABILITY OF LLM AGENTS", "paper_id": "paper_100003", "abstract": "Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents raise significant concerns o...	{ "score": 5, "author_id": "author_612069", "papers": [ { "title": "GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing", "abstract": "The application of Vision-Language Models (VLMs) in remote sensing (RS) image understanding has achieved notable progress, de...	{ "score": 4, "author_id": "author_552312", "papers": [ { "title": "Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!", "abstract": "Fine-tuning on open-source Large Language Models (LLMs) with proprietary data is now a standard practice for downstrea...	paper_centric
{ "paper_title": "AUTOEVAL: A PRACTICAL FRAMEWORK FOR AUTONOMOUS EVALUATION OF MOBILE AGENTS", "paper_id": "paper_100004", "abstract": "Comprehensive evaluation of mobile agents can significantly advance their development and real-world applicability. However, existing benchmarks lack practicality and scalability...	{ "score": 5, "author_id": "author_486436", "papers": [ { "title": "Ensemble Learning for Graph Neural Networks", "abstract": "Graph Neural Networks (GNNs) have shown success in various fields for learning from graph-structured data. This paper investigates the application of ensemble learning tec...	{ "score": 3, "author_id": "author_483984", "papers": [ { "title": "SceneGenAgent: Precise Industrial Scene Generation with Coding Agent", "abstract": "The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown signific...	paper_centric
{ "paper_title": "Abundance-Aware Set Transformer for Microbiome Sample Embedding", "paper_id": "paper_100005", "abstract": "Microbiome sample representation to input into LLMs is essential for downstream tasks such as phenotype prediction and environmental classification. While prior studies have explored embedd...	{ "score": 3, "author_id": "author_586605", "papers": [ { "title": "Generating Highly Designable Proteins with Geometric Algebra Flow Matching", "abstract": "We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we...	{ "score": 1, "author_id": "author_521654", "papers": [ { "title": "ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area", "abstract": "Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chem...	paper_centric
{ "paper_title": "Abundance-Aware Set Transformer for Microbiome Sample Embedding", "paper_id": "paper_100005", "abstract": "Microbiome sample representation to input into LLMs is essential for downstream tasks such as phenotype prediction and environmental classification. While prior studies have explored embedd...	{ "score": 3, "author_id": "author_442679", "papers": [ { "title": "Bidirectional Representations Augmented Autoregressive Biological Sequence Generation: Application in De Novo Peptide Sequencing", "abstract": "Autoregressive (AR) models, common in sequence generation, are limited in many biologi...	{ "score": 1, "author_id": "author_521654", "papers": [ { "title": "ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area", "abstract": "Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chem...	paper_centric
{ "paper_title": "Accounting for Uncertainty in Machine Learning Surrogates: A Gauss-Hermite Quadrature Approach to Reliability Analysis", "paper_id": "paper_100006", "abstract": "Machine learning surrogates are increasingly employed to replace expensive computational models for physics-based reliability analysis...	{ "score": 4, "author_id": "author_459532", "papers": [ { "title": "IMPROVING UNCERTAINTY ESTIMATION THROUGH SEMANTICALLY DIVERSE LANGUAGE GENERATION", "abstract": "Large language models (LLMs) can suffer from hallucinations when generating text. These hallucinations impede various applications in...	{ "score": 2, "author_id": "author_609376", "papers": [ { "title": "Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET", "abstract": "Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to i...	paper_centric
{ "paper_title": "Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models", "paper_id": "paper_100007", "abstract": "Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs of...	{ "score": 4, "author_id": "author_458872", "papers": [ { "title": "Model-based Large Language Model Customization as Service", "abstract": "Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applicat...	{ "score": 2, "author_id": "author_526832", "papers": [ { "title": "Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the \"think...	paper_centric
{ "paper_title": "Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models", "paper_id": "paper_100007", "abstract": "Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs of...	{ "score": 4, "author_id": "author_557730", "papers": [ { "title": "AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation", "abstract": "In modern large language models (LLMs), LLM alignment is of crucial importance and is typically achieved through methods such as rein...	{ "score": 2, "author_id": "author_526832", "papers": [ { "title": "Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the \"think...	paper_centric

End of preview.

YAML Metadata Warning:The task_categories "information-retrieval" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Overview

This repository contains evaluation data for reviewer assignment / matching in pairwise format, organized into two complementary perspectives:

evaluation_pc (Paper-Centric pairwise): pairwise comparisons constructed from a paper-centric view (i.e., for each paper, compare candidate reviewers in pairs).
evaluation_rc (Reviewer-Centric pairwise): pairwise comparisons constructed from a reviewer-centric view (i.e., for each reviewer, compare candidate papers in pairs).

Status / Release Plan

🚧 Pointwise data is still being consolidated.
We expect to release the pointwise portion in ~2–3 days.

File Structure

evaluation_pc/ : paper-centric pairwise evaluation data
evaluation_rc/ : reviewer-centric pairwise evaluation data
(Coming soon) pointwise/ : pointwise evaluation data

Notes

If you use this dataset, please cite this repository (citation info can be added here later).
For questions or issues, please open a GitHub/HF issue in the repository.

Downloads last month: 10

Total file size:

16.5 MB