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Gnociew
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LR-bench

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Text Classification

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<author_id: string, papers: list<item: struct<abstract: string, title: string>>>
to
{'abstract': Value('string'), 'paper_id': Value('string'), 'paper_title': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2224, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, 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 2092, 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<abstract: string, title: string>>>
              to
              {'abstract': Value('string'), 'paper_id': Value('string'), 'paper_title': 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 1339, 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 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, 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 1858, 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

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anchor dict	positive dict	negative dict	type string
{ "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 inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of ...	{ "author_id": "author_695265", "papers": [ { "abstract": "Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression. However, in existing methods, the image segmentation process lead...	{ "author_id": "author_592279", "papers": [ { "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 = 5.34°F igure 1. This paper introduces CCMNet, a framework for cross-camera color constancy. CCMNet uses pre-calibrated...	paper_centric
{ "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 inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of ...	{ "author_id": "author_695265", "papers": [ { "abstract": "Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression. However, in existing methods, the image segmentation process lead...	{ "author_id": "author_656045", "papers": [ { "abstract": "The image may be a machine-generated image depicting a birthday party scene. There are many characters in the picture, giving people a lively feeling. The color combination is very harmonious, and the overall image is very clean and tidy. The figu...	paper_centric
{ "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 still demonstrate good performance in tasks such as image classification and object detection. We propose a novel separable self-at...	{ "author_id": "author_445191", "papers": [ { "abstract": "This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and...	{ "author_id": "author_610649", "papers": [ { "abstract": "Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in a wide range of applications, including web navigation, software development, and embodied control. While most LLMs are limited in several key age...	paper_centric
{ "abstract": "Semantic relations among entities are a widely accepted method for relation extraction. PromptORE (Prompt-based Open Relation Extraction) was designed to improve relation extraction with Large Language Models on generalistic documents. However, it is less effective when applied to historical documents,...	{ "author_id": "author_400040", "papers": [ { "abstract": "Low-light image enhancement (LLIE) aims to improve the perceptibility and interpretability of images captured in poorly illuminated environments. Existing LLIE methods often fail to capture the non-local self-similarity and long-range dependencies...	{ "author_id": "author_264483", "papers": [ { "abstract": "Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task,...	paper_centric
{ "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 on AI safety due to their autonomous and non-deterministic behavior, as well as continuous evolvin...	{ "author_id": "author_612069", "papers": [ { "abstract": "The application of Vision-Language Models (VLMs) in remote sensing (RS) image understanding has achieved notable progress, demonstrating the basic ability to recognize and describe geographical entities. However, existing RS-VLMs are mostly limite...	{ "author_id": "author_552312", "papers": [ { "abstract": "Fine-tuning on open-source Large Language Models (LLMs) with proprietary data is now a standard practice for downstream developers to obtain task-specific LLMs. Surprisingly, we reveal a new and concerning risk along with the practice: the creator...	paper_centric
{ "abstract": "Comprehensive evaluation of mobile agents can significantly advance their development and real-world applicability. However, existing benchmarks lack practicality and scalability due to the extensive manual effort in defining task reward signals and implementing evaluation codes. We propose AutoEval, a...	{ "author_id": "author_486436", "papers": [ { "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 techniques to improve the performance and robustness of Graph Neural Networks (...	{ "author_id": "author_483984", "papers": [ { "abstract": "The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial scenes ...	paper_centric
{ "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 embeddingbased representations of each microbiome sample, most rely on simple averaging over sequence embeddings, often ...	{ "author_id": "author_586605", "papers": [ { "abstract": "We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture f...	{ "author_id": "author_521654", "papers": [ { "abstract": "Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully hand...	paper_centric
{ "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 embeddingbased representations of each microbiome sample, most rely on simple averaging over sequence embeddings, often ...	{ "author_id": "author_442679", "papers": [ { "abstract": "Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks like de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies...	{ "author_id": "author_521654", "papers": [ { "abstract": "Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully hand...	paper_centric
{ "abstract": "Machine learning surrogates are increasingly employed to replace expensive computational models for physics-based reliability analysis. However, their use introduces epistemic uncertainty from model approximation errors, which couples with aleatory uncertainty in model inputspotentially compromising th...	{ "author_id": "author_459532", "papers": [ { "abstract": "Large language models (LLMs) can suffer from hallucinations when generating text. These hallucinations impede various applications in society and industry by making LLMs untrustworthy. Current LLMs generate text in an autoregressive fashion by pre...	{ "author_id": "author_609376", "papers": [ { "abstract": "Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on building a subject-specific model of healthy appearance to which a s...	paper_centric
{ "abstract": "Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgments. In this work, we examine logical preference consistency as a foundational requirement for building more dependable LLM...	{ "author_id": "author_458872", "papers": [ { "abstract": "Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data...	{ "author_id": "author_526832", "papers": [ { "abstract": "Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the \"think-thenanswer\" paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations ...	paper_centric
{ "abstract": "Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgments. In this work, we examine logical preference consistency as a foundational requirement for building more dependable LLM...	{ "author_id": "author_557730", "papers": [ { "abstract": "In modern large language models (LLMs), LLM alignment is of crucial importance and is typically achieved through methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO). However, in most existing ...	{ "author_id": "author_526832", "papers": [ { "abstract": "Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the \"think-thenanswer\" paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations ...	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.

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