Search Results for author:
Found 10 papers, 4 papers with code
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge
Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling.
FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM Extraction
To address this challenge, we propose an innovative data augmentation methodology grounded in a multi-agent collaborative framework.
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts
Evaluating the ability of large language models (LLMs) to handle extended contexts is critical, particularly for retrieving information relevant to specific queries embedded within lengthy inputs.
CJEval: A Benchmark for Assessing Large Language Models Using Chinese Junior High School Exam Data
Online education platforms have significantly transformed the dissemination of educational resources by providing a dynamic and digital infrastructure.
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
To address these issues, we propose a novel large language model specifically designed for the Chinese financial domain, named SNFinLLM.
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries.
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing
Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries.
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries.
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation.
