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Found 454 papers, 186 papers with code
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases
Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.
Iterative GNN-based Decoder for Question Generation
However, (1) they ignore the rich structure information that is hidden in the previously generated text.
A Progressive Framework for Role-Aware Rumor Resolution
Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation.
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack
To reduce the potential side effects of using defense modules, we further propose a novel forgetting restricted adversarial training, which filters out bad adversarial examples that impair the performance of original ones.
PathQG: Neural Question Generation from Facts
Human evaluation also proves that our model is able to generate relevant and informative questions.
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving
With the advancements in natural language processing tasks, math word problem solving has received increasing attention.
An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News
We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists.
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning
Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training.
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation
CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks
Specifically, we re-formulate both token and sentence classification tasks into a unified language modeling task, and map label spaces of different tasks into the same vocabulary space.
A Structure-Aware Argument Encoder for Literature Discourse Analysis
Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context.
RMB: Comprehensively Benchmarking Reward Models in LLM Alignment
However, the current evaluation of RMs may not directly correspond to their alignment performance due to the limited distribution of evaluation data and evaluation methods that are not closely related to alignment objectives.
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness.
When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning
However, the absence of meaningful benchmark datasets and standardized evaluation procedures for MAG representation learning has impeded progress in this field.
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations.
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding
However, we argue that this formulation does not adequately convey the complete reading order information in the layout, which may potentially lead to performance decline in downstream VrD tasks.
Ranked #1 on
Key Information Extraction
on CORD
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting
To this end, in this paper, we formulate the Federated Labor Market Forecasting (FedLMF) problem and propose a Meta-personalized Convergence-aware Clustered Federated Learning (MPCAC-FL) framework to provide accurate and timely collaborative talent demand and supply prediction in a privacy-preserving way.
MambaEviScrib: Mamba and Evidence-Guided Consistency Make CNN Work Robustly for Scribble-Based Weakly Supervised Ultrasound Image Segmentation
Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment, diagnosis, and treatment.
AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure
Large language model (LLM)-based AI delegates are increasingly utilized to act on behalf of users, assisting them with a wide range of tasks through conversational interfaces.
Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry
By identifying and excluding dynamic elements from the mapping process, this segmentation enables the creation of a dense 3D map that accurately represents the static background only.
3D Scene Reconstruction
Simultaneous Localization and Mapping
Turn Every Application into an Agent: Towards Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents
This framework also facilitates the creation and expansion of APIs through automated exploration of applications.
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation
Then, we deploy contrastive learning to train the CLSP encoder to effectively represent precise state information.
Empirical Insights on Fine-Tuning Large Language Models for Question-Answering
Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can then be fine-tuned for the question-answering (QA) task.
Online Learning via Memory: Retrieval-Augmented Detector Adaptation
This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model.
Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation
Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals.
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator
Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs).
WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models
Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operation-ready approach.
Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization
Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized.
Ranked #1 on
Multiview Detection
on CVCS
Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data
Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Proximal Policy Optimization (PPO) that require extensive hyper-parameter tuning and present challenges in sample efficiency and stability.
Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction
This paper presents the pioneering effort on this task using a retrieval-based classification approach.
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts
This paper introduces a learning-based algorithm tailored to foster safe and efficient driving behaviors across varying levels of traffic flows in roundabouts.
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
The combination of photometric augmentation, TFL loss, and MC-dropout produced the best results, although dropout alone led to more false negatives in subsequent year predictions.
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries.
UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documents
The recognition of named entities in visually-rich documents (VrD-NER) plays a critical role in various real-world scenarios and applications.
Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360°
To the best of our knowledge, our model is the first parametric 3D full-head that achieves 360{\deg} free-view synthesis, image-based fitting, appearance editing, and animation within a single model.
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities
Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection.
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation
Therefore, investigating how to better comprehend the negative labeling of unobserved interactions in academic reviewer recommendations is a significant challenge.
FINER++: Building a Family of Variable-periodic Functions for Activating Implicit Neural Representation
By initializing the bias of the neural network with different ranges, sub-functions with various frequencies in the variable-periodic function are selected for activation.
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks
BGML incorporates a multi-granular hierarchical progressive learning mechanism rooted in feature graph grain learning to mitigate potential conflict between memorization and forgetting in graph memory learning.
The Vision of Autonomic Computing: Can LLMs Make It a Reality?
The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing environments.
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series, referred to as distribution shift.
E5-V: Universal Embeddings with Multimodal Large Language Models
We propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs.
Power Optimization and Deep Learning for Channel Estimation of Active IRS-Aided IoT
In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS.
Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending
To tackle this issue, we introduce the Neural Poisson Solver, a plug-and-play and universally applicable framework across different signal dimensions for blending visual signals represented by INRs.
What's Wrong with Your Code Generated by Large Language Models? An Extensive Study
The increasing development of large language models (LLMs) in code generation has drawn significant attention among researchers.
An Earth Rover dataset recorded at the ICRA@40 party
The ICRA conference is celebrating its $40^{th}$ anniversary in Rotterdam in September 2024, with as highlight the Happy Birthday ICRA Party at the iconic Holland America Line Cruise Terminal.
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding
FlowLearn contains complex scientific flowcharts and simulated flowcharts.
Optical Character Recognition (OCR)
Visual Question Answering (VQA)
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining
In recent years, the rise of generative self-supervised learning (SSL) paradigms has exhibited impressive performance across visual, language, and multi-modal domains.
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
We present a deep frequency derivative learning framework, DERITS, for non-stationary time series forecasting.
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation
Recent advancements in Large Language Models have transformed ML/AI development, necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation (RAG) systems.
SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance
As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research.
Discovering Common Information in Multi-view Data
We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from G\'acs-K\"orner common information in information theory.
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions
To the best of our knowledge, SciDMT is the largest corpus for scientific entity mention detection.
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation
Recent advances in retrieval-augmented generation have significantly improved the performance of question-answering systems, particularly on factoid '5Ws' questions.
Aligning Large Language Models from Self-Reference AI Feedback with one General Principle
In aligning large language models (LLMs), utilizing feedback from existing advanced AI rather than humans is an important method to scale supervisory signals.
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs).
Efficient Sequential Decision Making with Large Language Models
In this paper, we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making.
Toward Optimal LLM Alignments Using Two-Player Games
We theoretically demonstrate that this iterative reinforcement learning optimization converges to a Nash Equilibrium for the game induced by the agents.
Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases
Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases.
Uncertainty Aware Learning for Language Model Alignment
As instruction-tuned large language models (LLMs) evolve, aligning pretrained foundation models presents increasing challenges.
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community.
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains.
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian
3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis.
Multi-View People Detection in Large Scenes via Supervised View-Wise Contribution Weighting
Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets.
Ranked #2 on
Multiview Detection
on CVCS
P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation
This adjustment enhances the structure for multi-head attention computation, leading to enhanced network performance and CBLA is a plug-and-play module.
MGDA Converges under Generalized Smoothness, Provably
We provide a comprehensive convergence analysis of these algorithms and show that they converge to an $\epsilon$-accurate Pareto stationary point with a guaranteed $\epsilon$-level average CA distance (i. e., the gap between the updating direction and the CA direction) over all iterations, where totally $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-4})$ samples are needed for deterministic and stochastic settings, respectively.
Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh
This approach reduces the need to design various algorithms for different types of Gaussian manipulation.
BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to be optimal or near-optimal solutions to the optimization problem.
ASI++: Towards Distributionally Balanced End-to-End Generative Retrieval
Generative retrieval, a promising new paradigm in information retrieval, employs a seq2seq model to encode document features into parameters and decode relevant document identifiers (IDs) based on search queries.
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models.
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score.
MetaRM: Shifted Distributions Alignment via Meta-Learning
These two issues can be united as a challenge posed by the shifted distribution of the environment.
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning
The entire framework has a message module and an action module.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning
To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection.
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges.
Wills Aligner: A Robust Multi-Subject Brain Representation Learner
We meticulously evaluate the performance of our approach across coarse-grained and fine-grained visual decoding tasks.
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks.
Length Generalization of Causal Transformers without Position Encoding
Generalizing to longer sentences is important for recent Transformer-based language models.
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation.
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors.
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
We prove that our algorithm finds an $\epsilon$-stationary point with a computational complexity of $\mathcal O(\epsilon^{-3k_*-5})$, where $k_*$ is the parameter of the Cressie-Read divergence.
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Specifically, to solve the challenges due to dependence among adaptive update, unbounded gradient estimate and Lipschitz constant, we demonstrate that the first-order term in the descent lemma converges and its denominator is upper bounded by a function of gradient norm.
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models
The generated demos strategically interpolate between existing demos and the given query, transforming the query from OOD to ID.
Information Cascade Prediction under Public Emergencies: A Survey
With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies.
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals
We first empirically show that the features of either clean signals or adversarial perturbations are redundant and span in low-dimensional linear subspaces respectively with minimal overlap, and the classical low-dimensional subspace projection can suppress perturbation features out of the subspace of clean signals.
Non-negative Contrastive Learning
In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features.
EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
UV Gaussians: Joint Learning of Mesh Deformation and Gaussian Textures for Human Avatar Modeling
Therefore, we propose UV Gaussians, which models the 3D human body by jointly learning mesh deformations and 2D UV-space Gaussian textures.
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels.
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
We propose Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently and faithfully reason over structured environments.
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs).
Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides
Effective incident management is pivotal for the smooth operation of enterprises-level cloud services.
RECOST: External Knowledge Guided Data-efficient Instruction Tuning
When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples.
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions
In this paper, drawing inspiration from recent works that LLMs are sensitive to the design of the instructions, we utilize instructions in code style, which are more structural and less ambiguous, to replace typically natural language instructions.
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
Adversarial misuse, particularly through `jailbreaking' that circumvents a model's safety and ethical protocols, poses a significant challenge for Large Language Models (LLMs).
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain.
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated text, we leverage the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
Unveiling Linguistic Regions in Large Language Models
Furthermore, this core region exhibits significant dimensional dependence, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence.
On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe
Here we re-purpose the reverse dictionary task as a case study to probe LLMs' capacity for conceptual inference.
$Se^2$: Sequential Example Selection for In-Context Learning
The remarkable capability of large language models (LLMs) for in-context learning (ICL) needs to be activated by demonstration examples.
Text Diffusion with Reinforced Conditioning
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio.
MSynFD: Multi-hop Syntax aware Fake News Detection
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution
To address this issue, we propose a cost-effective preference learning strategy, optimizing reward models by distinguishing between human and machine translations.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Recent advancements in large language models (LLMs) have opened new pathways for many domains.
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks.
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks.
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios.
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs.