Search Results for author:
Found 35 papers, 15 papers with code
R2D2: Remembering, Reflecting and Dynamic Decision Making for Web Agents
The proliferation of web agents necessitates advanced navigation and interaction strategies within complex web environments.
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
Research on tool calling has gathered momentum, but evaluation benchmarks and datasets representing the complexity of the tasks have lagged behind.
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
In this paper, we present TabSketchFM, a neural tabular model for data discovery over data lakes.
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
Formally Specifying the High-Level Behavior of LLM-Based Agents
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
LakeBench: Benchmarks for Data Discovery over Data Lakes
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.
Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases
Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data.
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
Abstract Meaning Representation
Knowledge Base Question Answering
+1
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering
Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.
A Scalable AutoML Approach Based on Graph Neural Networks
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
Ranked #2 on
Question Answering
on TempQA-WD
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.
Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding
Code understanding is an increasingly important application of Artificial Intelligence.
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
Learning to Guide a Saturation-Based Theorem Prover
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on
Relation Linking
on QALD-7
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map
The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations.
A Toolkit for Generating Code Knowledge Graphs
We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
Explainable Deep RDFS Reasoner
Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules.
An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.
Ranked #1 on
Automated Theorem Proving
on HolStep (Conditional)
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
AltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database
Currently, there is no online Arabic handwriting database with large lexicon, high coverage, large number of writers and training/testing data.
Large Vocabulary Arabic Online Handwriting Recognition System
In this paper, we introduce a fully-fledged Hidden Markov Model (HMM) based system for Arabic online handwriting recognition that provides solutions for most of the difficulties inherent in recognizing the Arabic script.
