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Found 25 papers, 17 papers with code
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits
In this paper, we optimize the task by constructing MBTIBench, the first manually annotated high-quality MBTI personality detection dataset with soft labels, under the guidance of psychologists.
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types
To address these challenges, we propose a QA benchmark for scientific tables and text with diverse reasoning types (SciTaT).
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
Our survey organizes the findings into three core components: Game Framework, Social Agent, and Evaluation Protocol.
SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA).
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
To address this, inspired by transfer learning, we propose In-Context Transfer Learning (ICTL), which synthesizes target task demonstrations by transferring labeled demonstrations from similar source tasks.
DAC: Decomposed Automation Correction for Text-to-SQL
Therefore, in this paper, we propose to employ the decomposed correction to enhance text-to-SQL performance.
FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats
Most existing methods employ a fixed tabular format to represent the table, which could limit the performance.
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
We investigate how vocabulary size impacts LLM scaling laws by training models ranging from 33M to 3B parameters on up to 500B characters with various vocabulary configurations.
RegMix: Data Mixture as Regression for Language Model Pre-training
With the fitted regression model, we simulate the top-ranked mixture and use it to train a large-scale model with orders of magnitude more compute.
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL.
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research.
Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes
Numerical reasoning is an essential ability for NLP systems to handle numeric information.
A Survey of Table Reasoning with Large Language Models
In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning.
Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning
So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs.
Controllable Data Augmentation for Context-Dependent Text-to-SQL
In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results.
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.
From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Empirical results on various benchmarks validate that the integration of SQL execution leads to significant improvements in zero-shot scenarios, particularly in table reasoning.
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables.
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions
Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain.
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems.
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings.
HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser
This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.
Ranked #1 on
UCCA Parsing
on CoNLL 2019
Data2Text Studio: Automated Text Generation from Structured Data
Data2Text Studio is a platform for automated text generation from structured data.
