Papers by Riyaz Bhat

4 papers
Prompting with Pseudo-Code Instructions (2023.emnlp-main)

Copied to clipboard

Challenge: ambiguity in natural language can hinder performance of large language models.
Approach: They manually create a dataset of pseudo-code prompts for 132 different classification, QA, and generative language tasks, sourced from the Super-NaturalInstructions dataset.
Outcome: The pseudo-code prompts improve the performance of two LLM families, BLOOM and CodeGen.
DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection (2022.emnlp-main)

Copied to clipboard

Challenge: Documentlevel NLI is an important problem for many tasks including verification of factual correctness of documents.
Approach: They propose a document-level natural language inference model that builds a hierarchical document graph enriched through inter-sentence relations and performs paragraph pruning using the novel SubGraph Pooling layer.
Outcome: The proposed model performs on a legal judicial reasoning task with a dataset enriched with document graphs and a proposed evidence selection algorithm.
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)

Copied to clipboard

Challenge: Question Answering (QA) is a major area of research in Natural Language Processing (NLP)
Approach: They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods .
Outcome: The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods.
Semi-Structured Object Sequence Encoders (2023.findings-emnlp)

Copied to clipboard

Challenge: Semi-structured object sequences are often represented as a sequence of key-value pairs over time . authors propose a two-part approach that takes each key independently and encodes a representation of its values over time.
Approach: They propose a two-part approach that first considers each key independently and encodes a representation of its values over time.
Outcome: The proposed approach outperforms existing methods on multiple prediction tasks using real-world data.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations