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Found 20 papers, 1 papers with code
Automatic Prompt Selection for Large Language Models
Our approach consists of three steps: (1) clustering the training data and generating candidate prompts for each cluster using an LLM-based prompt generator; (2) synthesizing a dataset of input-prompt-output tuples for training a prompt evaluator to rank the prompts based on their relevance to the input; (3) using the prompt evaluator to select the best prompt for a new input at test time.
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition
In this new era of rapid AI development, especially in language processing, the demand for AI in the legal domain is increasingly critical.
Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures
This paper introduces a new IncidentAI dataset for safety prevention.
When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dust
Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia.
Emotion-Cause Pair Extraction as Question Answering
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential emotion-cause pairs of a document without any annotation of emotion or cause clauses.
CinPatent: Datasets for Patent Classification
We release the two new datasets with the code of the baselines.
Multi Label Text Classification
Multi-Label Text Classification
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Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances
Meetings are a universal process to make decisions in business and project collaboration.
Improving Document Image Understanding with Reinforcement Finetuning
In this paper, we investigate the problem of improving the performance of Artificial Intelligence systems in understanding document images, especially in cases where training data is limited.
Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents
This paper introduces a new information extraction model for business documents.
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous.
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation
To support the picker, we design two label creation methods (soft and hard labels), which can work in cases of no annotation data for the omitted tokens.
Robust Deep Reinforcement Learning for Extractive Legal Summarization
Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles.
A Span Extraction Approach for Information Extraction on Visually-Rich Documents
Firstly, we introduce a new query-based IE model that employs span extraction instead of using the common sequence labeling approach.
Sentence Compression as Deletion with Contextual Embeddings
Sentence compression is the task of creating a shorter version of an input sentence while keeping important information.
Transfer Learning for Information Extraction with Limited Data
This paper presents a practical approach to fine-grained information extraction.
Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network
This paper presents a study of employing Ranking SVM and Convolutional Neural Network for two missions: legal information retrieval and question answering in the Competition on Legal Information Extraction/Entailment.
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization
This paper presents VSoLSCSum, a Vietnamese linked sentence-comment dataset, which was manually created to treat the lack of standard corpora for social context summarization in Vietnamese.
Lexical-Morphological Modeling for Legal Text Analysis
In the context of the Competition on Legal Information Extraction/Entailment (COLIEE), we propose a method comprising the necessary steps for finding relevant documents to a legal question and deciding on textual entailment evidence to provide a correct answer.
