54 citations · 111 across the 23 of their papers we have counts for
25 papers
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…
Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems
Gaetano Rossiello, Dharmashankar Subramanian
Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real-time streaming environments…
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…
ConstrainedSQL: Training LLMs for Text2SQL via Constrained Reinforcement Learning
Weiqin Chen, Nhan Huu Pham, Michael Robert Glass +4
Reinforcement learning (RL) has demonstrated significant promise in enhancing the reasoning capabilities of Text2SQL LLMs, especially with advanced algorithms such as GRPO and DAPO…
GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models
Mattia Tritto, Giuseppe Farano, Dario Di Palma +4
Text-to-SQL, the task of translating natural language questions into SQL queries, has significantly advanced with the introduction of Large Language Models (LLMs), broadening datab…
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…
Extractive Schema Linking for Text-to-SQL
Michael Glass, Mustafa Eyceoz, Dharmashankar Subramanian +3
Text-to-SQL is emerging as a practical interface for real world databases. The dominant paradigm for Text-to-SQL is cross-database or schema-independent, supporting application sch…
Open Government Data Corpus for Table Search
Michael Glass, Sugato Bagchi, Oktie Hassanzadeh +2
Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema,…
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…
Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case
Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello +7
A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategie…
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…
Zero-shot Slot Filling with DPR and RAG
Michael Glass, Gaetano Rossiello, Alfio Gliozzo
The ability to automatically extract Knowledge Graphs (KG) from a given collection of documents is a long-standing problem in Artificial Intelligence. One way to assess this capabi…
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…
Knowledge Graph Embeddings and Explainable AI
Federico Bianchi, Gaetano Rossiello, Luca Costabello +2
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we intr…