54 citations · 111 across the 23 of their papers we have counts for
17 papers · 1 filter
Test-Time Verification for Text-to-SQL via Outcome Reward Models
Mattia Tritto, Giuseppe Farano, Dario Di Palma +4
Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference str…
RAWR: Reward Assignment Without Rollouts in Verifiable Domains
Corentin Royer, Debarun Bhattacharjya, Anna Hedström +3
Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) provide a solution by scoring each…
Rationalization Models for Text-to-SQL
Gaetano Rossiello, Nhan Pham, Michael Glass +2
We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-SQL model fine-tuning. These rationales consist of intermediate SQL statements and expl…
Retrieval-Based Transformer for Table Augmentation
Michael Glass, Xueqing Wu, Ankita Rajaram Naik +2
Data preparation, also called data wrangling, is considered one of the most expensive and time-consuming steps when performing analytics or building machine learning models. Prepar…
KnowGL: Knowledge Generation and Linking from Text
Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya +2
We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG…
Re2G: Retrieve, Rerank, Generate
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury +3
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-para…
KGI: An Integrated Framework for Knowledge Intensive Language Tasks
Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello +2
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks,…
A Generative Model for Relation Extraction and Classification
Jian Ni, Gaetano Rossiello, Alfio Gliozzo +1
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question an…
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass +2
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In…
Semantic Answer Type and Relation Prediction Task (SMART 2021)
Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo +5
Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some proble…
Robust Retrieval Augmented Generation for Zero-shot Slot Filling
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury +1
Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is thr…
Generative Relation Linking for Question Answering over Knowledge Bases
Gaetano Rossiello, Nandana Mihindukulasooriya, Ibrahim Abdelaziz +4
Relation linking is essential to enable question answering over knowledge bases. Although there are various efforts to improve relation linking performance, the current state-of-th…
Open Knowledge Graphs Canonicalization using Variational Autoencoders
Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya +2
Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples. Existing appro…
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar +27
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understandin…
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi +6
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowle…
Exploring Software Naturalness through Neural Language Models
Luca Buratti, Saurabh Pujar, Mihaela Bornea +8
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis…
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Claudio Greco, Alessandro Suglia, Pierpaolo Basile +2
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user co…