Search Results for author:
Found 14 papers, 6 papers with code
Measuring Social Norms of Large Language Models
This enables the direct comparison of the social understanding of large language models to humans, more specifically, elementary students.
Measuring Vision-Language STEM Skills of Neural Models
Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
Lyra: Orchestrating Dual Correction in Automated Theorem Proving
In addition, we introduce Conjecture Correction, an error feedback mechanism designed to interact with prover to refine formal proof conjectures with prover error messages.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on
Link Prediction
on UMLS
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision
To address this problem, we propose a novel search algorithm with combinatorial strategy \textbf{ComSearch}, which can compress the search space by excluding mathematically equivalent equations.
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information.
Ranked #3 on
Link Prediction
on UMLS
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs.
Generate & Rank: A Multi-task Framework for Math Word Problems
Math word problem (MWP) is a challenging and critical task in natural language processing.
Ranked #2 on
Math Word Problem Solving
on Math23K
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
Learning to Answer Ambiguous Questions with Knowledge Graph
In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation.
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records.
