Datasets:

ulab-ai
/

AcademicEval

url stringlengths 33 33	published stringdate 2024-04-25 00:00:00 2024-05-09 00:00:00	title stringlengths 16 144	abstract stringlengths 450 2.9k	gt stringlengths 450 2.9k	primary_cat stringclasses 48 values	paper_cat stringclasses 20 values	updated stringdate 2024-04-25 00:00:00 2024-05-09 00:00:00	main_content stringlengths 5.61k 156k	authors stringlengths 8 563	label stringclasses 1 value	cats listlengths 1 6
http://arxiv.org/abs/2404.16260v1	2024-04-25 00:00:00	OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search	In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement, and $>5\%$ ads CTR in...	In this paper, we present OmniSearchSage, a versatile and scalable system for understanding search queries, pins, and products for Pinterest search. We jointly learn a unified query embedding coupled with pin and product embeddings, leading to an improvement of $>8\%$ relevance, $>7\%$ engagement, and $>5\%$ ads CTR in...	cs.IR	LLM Fairness	2024-04-25 00:00:00	INTRODUCTION Pinterest’s mission is to bring everyone the inspiration to create a life they love. Search is one of the key surfaces on Pinterest where users seek inspiration spanning a wide range of interests, such as decorating their homes, planning weddings, or keeping up with the latest trends in beauty and fashion....	Prabhat Agarwal, Minhazul Islam Sk, Nikil Pancha, Kurchi Subhra Hazra, Jiajing Xu, Chuck Rosenberg	Original Paper	[ "cs.IR", "cs.AI", "cs.LG", "H.3.3" ]
http://arxiv.org/abs/2404.16277v1	2024-04-25 00:00:00	Causally Inspired Regularization Enables Domain General Representations	Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can identify domain-general (non-spurious) feature representations. For the standard input-output predictive setting, we categorize the set of graphs...	Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can identify domain-general (non-spurious) feature representations. For the standard input-output predictive setting, we categorize the set of graphs...	cs.LG	Knowledge AND Graph	2024-04-25 00:00:00	Introduction A key feature of machine learning is its capacity to generalize across new domains. When these domains present diﬀerent data distributions, the algorithm must leverage shared structural concepts to achieve outof-distribution (OOD) or out-of-domain generalization. This capability is vital in numerous import...	Olawale Salaudeen, Sanmi Koyejo	Original Paper	[ "cs.LG", "stat.ML" ]
http://arxiv.org/abs/2404.16283v1	2024-04-25 00:00:00	Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services	The advent of large language models (LLMs) has transformed text-based services, enabling capabilities ranging from real-time translation to AI-driven chatbots. However, existing serving systems primarily focus on optimizing server-side aggregate metrics like token generation throughput, ignoring individual user experie...	The advent of large language models (LLMs) has transformed text-based services, enabling capabilities ranging from real-time translation to AI-driven chatbots. However, existing serving systems primarily focus on optimizing server-side aggregate metrics like token generation throughput, ignoring individual user experie...	cs.DC	LLM Fairness	2024-04-25 00:00:00	Introduction Large language Models (LLMs) [4, 9, 21, 46, 51] have revolutionized natural language processing. By generating contextually relevant responses, they power a wide range of applications, more than 60% of which are centered around conversational interactions like chatbots, virtual assistants, language transla...	Jiachen Liu, Zhiyu Wu, Jae-Won Chung, Fan Lai, Myungjin Lee, Mosharaf Chowdhury	Original Paper	[ "cs.DC", "cs.LG" ]
http://arxiv.org/abs/2404.16294v1	2024-04-25 00:00:00	LLM-Based Section Identifiers Excel on Open Source but Stumble in Real World Applications	Electronic health records (EHR) even though a boon for healthcare practitioners, are growing convoluted and longer every day. Sifting around these lengthy EHRs is taxing and becomes a cumbersome part of physician-patient interaction. Several approaches have been proposed to help alleviate this prevalent issue either vi...	Electronic health records (EHR) even though a boon for healthcare practitioners, are growing convoluted and longer every day. Sifting around these lengthy EHRs is taxing and becomes a cumbersome part of physician-patient interaction. Several approaches have been proposed to help alleviate this prevalent issue either vi...	cs.CL	LLM Fairness	2024-04-25 00:00:00	Introduction Modern day healthcare systems are increasingly moving towards large scale adoption of maintaining electronic health records (EHR) of patients (Congress, 2009). EHRs help healthcare practitioners with relevant information about a patient such as history, medications, etc. However, in recent times this pract...	Saranya Krishnamoorthy, Ayush Singh, Shabnam Tafreshi	Original Paper	[ "cs.CL", "cs.AI" ]
http://arxiv.org/abs/2404.16297v1	2024-04-25 00:00:00	When Fuzzing Meets LLMs: Challenges and Opportunities	Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from...	Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from...	cs.SE	LLM Fairness	2024-04-25 00:00:00	INTRODUCTION Fuzzing is a promising technique for software bug detection [8, 26]. Large Language Models (LLM) are rapidly gaining popularity across various applications for their versatility and capability [14, 15]. From natural language processing [7, 22, 27] to code generation [19, 24], LLM’s broad utility is making ...	Yu Jiang, Jie Liang, Fuchen Ma, Yuanliang Chen, Chijin Zhou, Yuheng Shen, Zhiyong Wu, Jingzhou Fu, Mingzhe Wang, ShanShan Li, Quan Zhang	Original Paper	[ "cs.SE", "cs.AI" ]
http://arxiv.org/abs/2404.16300v1	2024-04-25 00:00:00	Reinforcement Learning with Generative Models for Compact Support Sets	"Foundation models contain a wealth of information from their vast number of\ntraining samples. Howe(...TRUNCATED)	"Foundation models contain a wealth of information from their vast number of\ntraining samples. Howe(...TRUNCATED)	cs.LG	Model AND Based AND Reinforcement AND Learning	2024-04-25 00:00:00	"Introduction Deep learning [10] is one of the most popular and successful methods for any task wher(...TRUNCATED)	Nico Schiavone, Xingyu Li	Original Paper	[ "cs.LG", "cs.CV" ]
http://arxiv.org/abs/2404.16301v1	2024-04-25 00:00:00	Style Adaptation for Domain-adaptive Semantic Segmentation	"Unsupervised Domain Adaptation (UDA) refers to the method that utilizes\nannotated source domain da(...TRUNCATED)	"Unsupervised Domain Adaptation (UDA) refers to the method that utilizes\nannotated source domain da(...TRUNCATED)	cs.CV	Semantic AND Segmentation AND Image	2024-04-25 00:00:00	"INTRODUCTION Neural Networks [1] and Transformers [2] have achieved great success in semantic segme(...TRUNCATED)	Ting Li, Jianshu Chao, Deyu An	Original Paper	[ "cs.CV" ]
http://arxiv.org/abs/2404.16302v1	2024-04-25 00:00:00	"CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection under Adverse Weather Conditio(...TRUNCATED)	"Cross-modality images that integrate visible-infrared spectra cues can\nprovide richer complementar(...TRUNCATED)	"Cross-modality images that integrate visible-infrared spectra cues can\nprovide richer complementar(...TRUNCATED)	cs.CV	Mamba	2024-04-25 00:00:00	"INTRODUCTION In an open and dynamic environment, object detection faces challenging weather conditi(...TRUNCATED)	Haoyuan Li, Qi Hu, You Yao, Kailun Yang, Peng Chen	Original Paper	[ "cs.CV", "cs.MM", "cs.RO", "eess.IV" ]
http://arxiv.org/abs/2404.16306v1	2024-04-25 00:00:00	TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models	"Text-conditioned image-to-video generation (TI2V) aims to synthesize a\nrealistic video starting fr(...TRUNCATED)	"Text-conditioned image-to-video generation (TI2V) aims to synthesize a\nrealistic video starting fr(...TRUNCATED)	cs.CV	Diffusion AND Model	2024-04-25 00:00:00	"Introduction Image-to-video (I2V) generation is an appealing topic with various applications, inclu(...TRUNCATED)	"Haomiao Ni, Bernhard Egger, Suhas Lohit, Anoop Cherian, Ye Wang, Toshiaki Koike-Akino, Sharon X. Hu(...TRUNCATED)	Original Paper	[ "cs.CV" ]
http://arxiv.org/abs/2404.16325v1	2024-04-25 00:00:00	Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models	"Despite the remarkable success of deep learning in medical imaging analysis,\nmedical image segment(...TRUNCATED)	"Despite the remarkable success of deep learning in medical imaging analysis,\nmedical image segment(...TRUNCATED)	cs.CV	Semantic AND Segmentation AND Image	2024-04-25 00:00:00	"Introduction Ultrasound is a popular medical imaging modality used to image a large variety of orga(...TRUNCATED)	"Hedda Cohen Indelman, Elay Dahan, Angeles M. Perez-Agosto, Carmit Shiran, Doron Shaked, Nati Daniel(...TRUNCATED)	Original Paper	[ "cs.CV", "cs.AI" ]

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AcademicEval Benchmark Introduction

We proposed AcademicEval, a live benchmark for evaluating LLMs over long-context generation tasks. AcademicEval adopts papers on arXiv to introduce several acadeic writing tasks with long-context inputs, i.e., Title, Abstract, Introduction, Related Work, wich covers a wide range of abstraction levels and require no manual labeling.

Comparing to existing long-context LLM benchmarks, our Comparing to existing long-context LLM benchmarks, our AcademicEval offers flexible length, automatic annotation, hierarchical abstraction, few-shot demonstrations, and live updates without data leakage risks.

🌟Note🌟: currently, for the ease of downloading, we only uploaded the test set of AcademicEval (The rest of AcademicEval, i.e., train and val set, can be accessed via AcademicEval Full). The data viewer above shows the preview data information of title-10K, abs-9K, and intro-8K. For the complete test set data, please check "Files and versions" in this page.

Benchmark	Avg Len	Automatic Annotation	Hierarchical Abstraction	Few-shot Demonstrations	Live Update
ZeroSCROLLS (Shaham et al., 2023)	~10K	✓	✘	✘	✘
L-Eval (An et al., 2023)	~8K	✘	✘	✘	✘
BAMBOO (Dong et al., 2023)	~16K	✘	✘	✘	✘
LongBench (Bai et al., 2023)	~8K	✘	✘	✓	✘
LooGLE (Li et al., 2023)	~20K	✘	✘	✘	✘
∞Bench (Zhang et al., 2024)	~200K	✘	✘	✘	✘
AcademicEval (ours)	Flexible	✓	✓	✓	✓

Dataset Structure

Data Settings

Title Writing
- title_10K
- title_30K
- title_31K_G
Abstract Writing
- abs_9K
- abs_28K
- abs_29K_G
Introduction Writing
- intro_8K
- intro_28K
- intro_28K_G
Related Work Writing
- related_34K
- related_53K
- related_53K_G

Main Data Fields

url: the url of the original paper on arXiv
title: the title of the paper
abstract: the abstract of the paper
authors: the authors of the paper
published: the publication timestamp of the paper
primary_cat: arXiv category
gt: the ground truth of the corresponding task
main_content: the main body of the paper (w/o the corresponding section content)
additional_info: the few-shot demonstrations from randomly selected papers (the data fields of each demonstration are the same as above)
additional_graph_info: the few-shot demonstrations with the co-author subgraph structure from co-author papers (the data fields of each demonstration are the same as above)

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