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Found 20 papers, 12 papers with code
LARGE: Legal Retrieval Augmented Generation Evaluation Tool
Recently, building retrieval-augmented generation (RAG) systems to enhance the capability of large language models (LLMs) has become a common practice.
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties
How capable are large language models (LLMs) in the domain of taxation?
Vision-Encoders (Already) Know What They See: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore
Recently, Large Vision-Language Models (LVLMs) show remarkable performance across various domains.
Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models
Here, we introduce KBL, a benchmark for assessing the Korean legal language understanding of LLMs, consisting of (1) 7 legal knowledge tasks (510 examples), (2) 4 legal reasoning tasks (288 examples), and (3) the Korean bar exam (4 domains, 53 tasks, 2, 510 examples).
Does Alignment Tuning Really Break LLMs' Internal Confidence?
Large Language Models (LLMs) have shown remarkable progress, but their real-world application necessitates reliable calibration.
On the Consideration of AI Openness: Can Good Intent Be Abused?
Open source is a driving force behind scientific advancement. However, this openness is also a double-edged sword, with the inherent risk that innovative technologies can be misused for purposes harmful to society.
SymBa: Symbolic Backward Chaining for Structured Natural Language Reasoning
To improve the performance and explainability of LLM-based natural language reasoning, structured reasoning can be applied to generate explicitly structured proofs.
NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus
Here we provide NESTLE, a no-code tool for large-scale statistical analysis of legal corpus.
Data-efficient End-to-end Information Extraction for Statistical Legal Analysis
Lawyers, for instance, search for appropriate precedents favorable to their clients, while the number of legal precedents is ever-growing.
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task.
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching
Semi-structured query systems for document-oriented databases have many real applications.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
OCR-free Document Understanding Transformer
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
Ranked #7 on
Key-value Pair Extraction
on SIBR
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents
On the other hand, this paper tackles the problem by going back to the basic: effective combination of text and layout.
Ranked #7 on
Relation Extraction
on FUNSD
Cost-effective End-to-end Information Extraction for Semi-structured Document Images
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.
Tractable loss function and color image generation of multinary restricted Boltzmann machine
The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics.
Spatial Dependency Parsing for Semi-Structured Document Information Extraction
Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.
Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing
Deep learning approaches to semantic parsing require a large amount of labeled data, but annotating complex logical forms is costly.
Post-OCR parsing: building simple and robust parser via BIO tagging
Parsing textual information embedded in images is important for various down- stream tasks.
Optical Character Recognition
Optical Character Recognition (OCR)
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.
