Datasets:

anikethh
/

Comparative-Idea-Evaluation

idea_1 string	idea_2 string	research_goal string	label int64
A transformer encoder-decoder pipeline is proposed for 3D dense captioning that disentangles the representations for object localization and contextual description. The approach begins by generating two distinct sets of queries from an encoded 3D point cloud. First, 'instance queries' are produced to capture the specif...	The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	1
The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	A transformer encoder-decoder pipeline is proposed for 3D dense captioning that disentangles the representations for object localization and contextual description. The approach begins by generating two distinct sets of queries from an encoded 3D point cloud. First, 'instance queries' are produced to capture the specif...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	0
A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	1
The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	0
A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	1
The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The primary research objective is to develop models capable of performing 3D dense captioning, which involves generating rich, context-aware natural language descriptions for multiple distinct objects within a complex 3D scene. Utilizing the Nr3D dataset, this research challenges systems to process 3D point cloud data ...	0
A unified framework is proposed to integrate contrastive vision-language learning with 3D caption generation in a single, end-to-end architecture. The approach consists of a 3D scene encoder, a text encoder, a contrastive learning module, and a multi-modal fusion decoder. The 3D scene encoder transforms an input point ...	A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	A unified framework is proposed to integrate contrastive vision-language learning with 3D caption generation in a single, end-to-end architecture. The approach consists of a 3D scene encoder, a text encoder, a contrastive learning module, and a multi-modal fusion decoder. The 3D scene encoder transforms an input point ...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A unified framework is proposed to integrate contrastive vision-language learning with 3D caption generation in a single, end-to-end architecture. The approach consists of a 3D scene encoder, a text encoder, a contrastive learning module, and a multi-modal fusion decoder. The 3D scene encoder transforms an input point ...	The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	A unified framework is proposed to integrate contrastive vision-language learning with 3D caption generation in a single, end-to-end architecture. The approach consists of a 3D scene encoder, a text encoder, a contrastive learning module, and a multi-modal fusion decoder. The 3D scene encoder transforms an input point ...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A novel transformer-based pipeline for 3D dense captioning is proposed, built on a paradigm of late aggregation to resolve the conflict between local feature precision and broad contextual awareness. The core of this approach is to disentangle the feature extraction process by simultaneously decoding two distinct sets ...	A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	A novel transformer-based pipeline for 3D dense captioning is proposed, built on a paradigm of late aggregation to resolve the conflict between local feature precision and broad contextual awareness. The core of this approach is to disentangle the feature extraction process by simultaneously decoding two distinct sets ...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A transformer-based encoder-decoder architecture is proposed to transform 3D object proposals into natural language descriptions, with a central focus on explicitly modeling and learning relative spatial relationships. The process begins by using a 3D object detector to decompose an input point cloud into a set of obje...	A novel end-to-end paradigm for 3D object detection from point clouds is proposed, which relies on sparse prediction to directly generate the final set of objects without requiring post-processing like non-maximum suppression. The architecture first processes the input point cloud by converting it into a voxel represen...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	A pre-training framework is proposed to learn universal multi-modal features from 3D point clouds and textual descriptions by establishing fine-grained interactions between the two modalities. The architecture consists of a point cloud encoder that generates object proposal features, a language encoder for word and sen...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A pre-training framework is proposed to learn universal multi-modal features from 3D point clouds and textual descriptions by establishing fine-grained interactions between the two modalities. The architecture consists of a point cloud encoder that generates object proposal features, a language encoder for word and sen...	The fundamental contribution is a unified, extensible software framework designed to standardize the interpretability of time series classification models. The system is proposed as an open-source library that consolidates various existing interpretation algorithms under a single, consistent Application Programming Int...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A pre-training framework is proposed to learn universal multi-modal features from 3D point clouds and textual descriptions by establishing fine-grained interactions between the two modalities. The architecture consists of a point cloud encoder that generates object proposal features, a language encoder for word and sen...	A cross-modal knowledge transfer framework for 3D dense captioning is proposed, structured as a teacher-student paradigm. The system is designed to distill knowledge from a multi-modal teacher network, which processes both 3D and 2D data, into a student network that operates exclusively on 3D data. During training, bot...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	1
A cross-modal knowledge transfer framework for 3D dense captioning is proposed, structured as a teacher-student paradigm. The system is designed to distill knowledge from a multi-modal teacher network, which processes both 3D and 2D data, into a student network that operates exclusively on 3D data. During training, bot...	A pre-training framework is proposed to learn universal multi-modal features from 3D point clouds and textual descriptions by establishing fine-grained interactions between the two modalities. The architecture consists of a point cloud encoder that generates object proposal features, a language encoder for word and sen...	The research objective is to develop and evaluate models for 3D dense captioning, a task that requires generating accurate, object-level natural language descriptions for a given 3D scene. Using 3D point cloud data from scanned indoor environments provided by the ScanRefer dataset, these models must identify multiple o...	0
A novel training paradigm is proposed to address the discrepancy between training objectives and inference-time behavior in sequence-to-sequence abstractive summarization models. The fundamental idea is to shift the training objective from a deterministic one-point target distribution, which only values the reference s...	A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	A novel training paradigm is proposed to address the discrepancy between training objectives and inference-time behavior in sequence-to-sequence abstractive summarization models. The fundamental idea is to shift the training objective from a deterministic one-point target distribution, which only values the reference s...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A two-stage framework for abstractive summarization is proposed, which reformulates the task as a reference-free quality estimation problem solved via contrastive learning. In the first stage, a pre-trained sequence-to-sequence generation model is used with a diverse sampling strategy, such as diverse beam search, to p...	A text-to-text transformer architecture is proposed to efficiently handle long input sequences by modifying the self-attention mechanism within the encoder, while the decoder remains a standard transformer. The core contribution is a novel attention mechanism that emulates a local/global attention pattern without requi...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A text-to-text transformer architecture is proposed to efficiently handle long input sequences by modifying the self-attention mechanism within the encoder, while the decoder remains a standard transformer. The core contribution is a novel attention mechanism that emulates a local/global attention pattern without requi...	A two-stage framework for abstractive summarization is proposed, which reformulates the task as a reference-free quality estimation problem solved via contrastive learning. In the first stage, a pre-trained sequence-to-sequence generation model is used with a diverse sampling strategy, such as diverse beam search, to p...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A two-stage framework for abstractive summarization is proposed, which reformulates the task as a reference-free quality estimation problem solved via contrastive learning. In the first stage, a pre-trained sequence-to-sequence generation model is used with a diverse sampling strategy, such as diverse beam search, to p...	A unified framework is proposed to treat every text-based language problem as a text-to-text task, where a model takes text as input and generates a new text as output. This approach enables the use of the same model, training objective, and decoding process across a diverse set of tasks, including classification, regr...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A unified framework is proposed to treat every text-based language problem as a text-to-text task, where a model takes text as input and generates a new text as output. This approach enables the use of the same model, training objective, and decoding process across a diverse set of tasks, including classification, regr...	A two-stage framework for abstractive summarization is proposed, which reformulates the task as a reference-free quality estimation problem solved via contrastive learning. In the first stage, a pre-trained sequence-to-sequence generation model is used with a diverse sampling strategy, such as diverse beam search, to p...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A novel approach for abstractive summarization is proposed, which operates within a single Transformer-based encoder-decoder network and is trained end-to-end through multi-task learning. The fundamental contribution is the use of a flexible and soft salience allocation as guidance, rather than a rigid extractive selec...	A unified framework is proposed to treat every text-based language problem as a text-to-text task, where a model takes text as input and generates a new text as output. This approach enables the use of the same model, training objective, and decoding process across a diverse set of tasks, including classification, regr...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A unified framework is proposed to treat every text-based language problem as a text-to-text task, where a model takes text as input and generates a new text as output. This approach enables the use of the same model, training objective, and decoding process across a diverse set of tasks, including classification, regr...	A novel approach for abstractive summarization is proposed, which operates within a single Transformer-based encoder-decoder network and is trained end-to-end through multi-task learning. The fundamental contribution is the use of a flexible and soft salience allocation as guidance, rather than a rigid extractive selec...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A novel approach for abstractive summarization is proposed, which operates within a single Transformer-based encoder-decoder network and is trained end-to-end through multi-task learning. The fundamental contribution is the use of a flexible and soft salience allocation as guidance, rather than a rigid extractive selec...	A unified pre-training framework is proposed for a single multi-layer Transformer network, designed to be jointly optimized using a combination of different unsupervised language modeling objectives. The core mechanism for this unification lies in the use of specific self-attention masks to control the context that a t...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A unified pre-training framework is proposed for a single multi-layer Transformer network, designed to be jointly optimized using a combination of different unsupervised language modeling objectives. The core mechanism for this unification lies in the use of specific self-attention masks to control the context that a t...	A novel approach for abstractive summarization is proposed, which operates within a single Transformer-based encoder-decoder network and is trained end-to-end through multi-task learning. The fundamental contribution is the use of a flexible and soft salience allocation as guidance, rather than a rigid extractive selec...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	A unified generative pre-training framework is proposed for bidirectional image-text generation, where both image-to-text and text-to-image synthesis are formulated as autoregressive sequence-to-sequence tasks within a single, parameter-sharing transformer model. The core mechanism relies on representing images as a se...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A unified generative pre-training framework is proposed for bidirectional image-text generation, where both image-to-text and text-to-image synthesis are formulated as autoregressive sequence-to-sequence tasks within a single, parameter-sharing transformer model. The core mechanism relies on representing images as a se...	A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	A pretraining-based encoder-decoder framework is proposed for sequence generation, which operates in a two-stage manner. The encoder utilizes a pre-trained BERT model to compute contextualized representations, H, for an input document X, formulated as H = BERT(x1, ..., xm). The decoding process is divided into two dist...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A pretraining-based encoder-decoder framework is proposed for sequence generation, which operates in a two-stage manner. The encoder utilizes a pre-trained BERT model to compute contextualized representations, H, for an input document X, formulated as H = BERT(x1, ..., xm). The decoding process is divided into two dist...	A method is proposed to reduce the computational complexity of processing long sequences in transformer architectures by progressively removing redundancies in the hidden states. This approach is founded on the observation that as information propagates through deeper layers of a transformer, the power spectrum of the ...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0
A pretraining framework is proposed based on an autoregressive blank infilling objective to create a general-purpose language model. The core mechanism involves corrupting an input text by sampling and replacing multiple continuous spans of tokens with a single [MASK] token. The model is then trained to reconstruct the...	A unified generative pre-training framework is proposed for bidirectional image-text generation, where both image-to-text and text-to-image synthesis are formulated as autoregressive sequence-to-sequence tasks within a single, parameter-sharing transformer model. The core mechanism relies on representing images as a se...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	1
A unified generative pre-training framework is proposed for bidirectional image-text generation, where both image-to-text and text-to-image synthesis are formulated as autoregressive sequence-to-sequence tasks within a single, parameter-sharing transformer model. The core mechanism relies on representing images as a se...	A pretraining framework is proposed based on an autoregressive blank infilling objective to create a general-purpose language model. The core mechanism involves corrupting an input text by sampling and replacing multiple continuous spans of tokens with a single [MASK] token. The model is then trained to reconstruct the...	The primary research goal is to develop and benchmark advanced neural models capable of abstractive text summarization, a task that requires generating novel, human-like summaries rather than simply extracting existing sentences. This objective is addressed using the large-scale CNN/Daily Mail dataset, which provides n...	0

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🔬 Comparative Idea Evaluation

Dataset accompanying our Findings of ACL 2026 paper: Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation.

Srujan P Mule · Aniketh Garikaparthi · Manasi Patwardhan

📄 ACL Anthology · arXiv

Research overview from Figure 1 of the paper. This release contains the comparison datasets; reasoning-training variants illustrated in the figure are not included.

🧠 What is this dataset for?

Given a research goal and two competing ideas, can a language model predict which idea will perform better before running the experiments?

Comparative Idea Evaluation provides benchmark-specific research goals, descriptions of competing methods, and preference labels grounded in empirical benchmark results. It supports training and evaluating models for pairwise idea selection and research-success forecasting.

📦 What is included?

This release contains seven JSONL files, exposed through four Hugging Face configurations:

Configuration	Difficulty	Train rows	Test rows
`sigma_1` (default)	Hard	12,240	988
`sigma_2`	Medium	6,922	568
`sigma_3`	Easy	2,076	182
`cross_domain`	Not stratified by sigma	—	1,410
Total		21,238	3,148

The main three configurations contain 22,976 augmented rows, corresponding to the paper's 11,488 comparisons before swap augmentation. The cross-domain configuration adds 1,410 rows (705 comparisons before augmentation). These counts describe the supplied records, not deduplicated comparisons.

The cross-domain test set provides additional evaluation on benchmarks outside the main domain, using result-reporting papers from 2024 onward. Separate reasoning datasets and model-generated response files are not part of this release.

🏷️ Labels and swap augmentation

Use the following convention for the files in this repository:

label = 1: idea_1 is better.
label = 0: idea_2 is better.

Each comparison is included in both orders, with the same research goal and an inverted label:

First idea	Second idea	Research goal	Label
A	B	G	1
B	A	G	0

These two rows describe the same underlying comparison. Avoid treating swapped rows as independent comparisons when calculating sample counts or creating new splits. Every released file is label-balanced and contains the swapped counterpart of each row.

🧾 Data fields

Field	Type	Description
`idea_1`	string	Description of the first candidate idea.
`idea_2`	string	Description of the second candidate idea.
`research_goal`	string	The benchmark-specific research objective.
`label`	integer	`1` if the first idea is better; `0` if the second is better.
`retrieval`	string	Retained from the source files; empty in this release.
`metric`	string	Benchmark metric name; present only in `cross_domain`.

Each line is a standalone JSON object. Original field names, record order, labels, and text are preserved.

⚙️ Dataset construction

Dataset-construction overview from Figure 2 of the paper.

The pipeline links PapersWithCode leaderboard entries to scientific papers, extracts method descriptions and research goals, and forms comparisons using normalized empirical results. Difficulty depends on the separation between the ideas' unified scores relative to the benchmark's standard deviation: 1σ pairs are closer in performance, while 3σ pairs are farther apart. See the paper for the score normalization, temporal splitting, and verification procedures.

🚀 Load the dataset

from datasets import load_dataset

# Hard comparisons: train and test splits
hard = load_dataset("anikethh/Comparative-Idea-Evaluation", "sigma_1")

# Medium and easy comparisons
medium = load_dataset("anikethh/Comparative-Idea-Evaluation", "sigma_2")
easy = load_dataset("anikethh/Comparative-Idea-Evaluation", "sigma_3")

# Cross-domain evaluation
cross_domain = load_dataset(
    "anikethh/Comparative-Idea-Evaluation", "cross_domain", split="test"
)

example = hard["train"][0]
winner = "idea_1" if example["label"] == 1 else "idea_2"

📋 Release notes and limitations

This release preserves the seven supplied comparison files without deduplication or resplitting. Only directory and file names have been simplified.
An exact-text check found repeated comparisons within the main files and one unordered comparison shared by the main train and test splits, matching on research goal and both idea descriptions. Ten distinct idea-description strings also occur in both splits. These are text-level checks, not a paper-identity audit. Account for this when evaluating leakage or constructing a cleaned benchmark.
No exact unordered comparison was shared between the cross-domain test and the main train/test files in the same check.
Labels describe relative performance on a particular benchmark; they are not general judgments of an idea's novelty, feasibility, or scientific value.
Method descriptions are derived from papers and may contain extraction errors. The distributed records do not include complete source-paper identifiers or raw empirical scores.

License

Original dataset contributions are released under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). See LICENSE. Third-party material remains subject to its applicable licenses and permissions.

The overview and construction figures are reproduced from the authors' arXiv paper, licensed under CC BY-SA 4.0. Provide attribution, link to the license, indicate modifications, and share adaptations under the applicable ShareAlike terms.

📄 Citation

@inproceedings{mule-etal-2026-teaching,
    title = "Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation",
    author = "Mule, Srujan P  and
      Garikaparthi, Aniketh  and
      Patwardhan, Manasi",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1918/",
    doi = "10.18653/v1/2026.findings-acl.1918",
    pages = "38491--38529"
}

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Paper for anikethh/Comparative-Idea-Evaluation

Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation

Paper • 2605.21491 • Published Apr 6