MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models

1 code implementation • 19 Oct 2023 • Deepak Nathani, David Wang, Liangming Pan, William Yang Wang

Language Models (LMs) have shown impressive performance in various natural language tasks.

Hallucination Mathematical Reasoning

Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

1 code implementation • 6 Aug 2023 • Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, William Yang Wang

Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.

Hallucination

Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings

no code implementations • ACL 2022 • Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, Partha Talukdar

When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages.

Attribute Sentence +3

Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures

1 code implementation • ICLR 2020 • Jatin Chauhan, Deepak Nathani, Manohar Kaul

We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples.

Active Learning Few-Shot Learning +2

Solving Partial Assignment Problems using Random Clique Complexes

1 code implementation • ICML 2018 • Charu Sharma, Deepak Nathani, Manohar Kaul

We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure.

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

2 code implementations • ACL 2019 • Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

Knowledge Base Completion Knowledge Graph Embeddings +2