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Found 10 papers, 6 papers with code
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data.
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Furthermore, compared to these competitive models, our proposed encoder enhances the downstream performance of NL2SQL models in 1-shot in-context learning scenarios by 1-2\% for GPT-3. 5-turbo, 4-8\% for CodeLlama-7B, and 2-3\% for CodeLlama-13B.
DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy.
Evaluating Cross-Domain Text-to-SQL Models and Benchmarks
In this paper, we conduct an extensive study of several prominent cross-domain text-to-SQL benchmarks and re-evaluate some of the top-performing models within these benchmarks, by both manually evaluating the SQL queries and rewriting them in equivalent expressions.
Performance Prediction for Multi-hop Questions
We study the problem of Query Performance Prediction (QPP) for open-domain multi-hop Question Answering (QA), where the task is to estimate the difficulty of evaluating a multi-hop question over a corpus.
Evaluating Open-Domain Question Answering in the Era of Large Language Models
The recent success of large language models (LLMs) for QA aggravates lexical matching failures since candidate answers become longer, thereby making matching with the gold answers even more challenging.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
In particular, we show that breaking down the generation problem into sub-problems and feeding the solutions of those sub-problems into LLMs can be an effective approach for significantly improving their performance.
Ranked #3 on
Text-To-SQL
on spider
On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction
Helpful reviews have been essential for the success of e-commerce services, as they help customers make quick purchase decisions and benefit the merchants in their sales.
Probing the Robustness of Pre-trained Language Models for Entity Matching
The paradigm of fine-tuning Pre-trained Language Models (PLMs) has been successful in Entity Matching (EM).
Ranked #3 on
Entity Resolution
on Amazon-Google
A Coherent Unsupervised Model for Toponym Resolution
The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.
