Search Results for author: Shivanshu Gupta

Found 8 papers, 6 papers with code

GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks

1 code implementation16 Nov 2023 Shivanshu Gupta, Clemens Rosenbaum, Ethan R. Elenberg

Further, we experiment with two variations: (1) fine-tuning gist models for each dataset and (2) multi-task training a single model on a large collection of datasets.

In-Context Learning

Leveraging Code to Improve In-context Learning for Semantic Parsing

1 code implementation16 Nov 2023 Ben Bogin, Shivanshu Gupta, Peter Clark, Ashish Sabharwal

In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization.

In-Context Learning Semantic Parsing

Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages

no code implementations25 May 2023 Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti

While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets.

Knowledge Distillation Machine Translation +2

Coverage-based Example Selection for In-Context Learning

1 code implementation24 May 2023 Shivanshu Gupta, Matt Gardner, Sameer Singh

In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informative about the test instance.

In-Context Learning Informativeness

Successive Prompting for Decomposing Complex Questions

no code implementations8 Dec 2022 Dheeru Dua, Shivanshu Gupta, Sameer Singh, Matt Gardner

The intermediate supervision is typically manually written, which can be expensive to collect.

Question Answering

Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation

1 code implementation16 Mar 2022 Shivanshu Gupta, Sameer Singh, Matt Gardner

A growing body of research has demonstrated the inability of NLP models to generalize compositionally and has tried to alleviate it through specialized architectures, training schemes, and data augmentation, among other approaches.

Data Augmentation Semantic Parsing

Unobserved Local Structures Make Compositional Generalization Hard

1 code implementation15 Jan 2022 Ben Bogin, Shivanshu Gupta, Jonathan Berant

While recent work has convincingly showed that sequence-to-sequence models struggle to generalize to new compositions (termed compositional generalization), little is known on what makes compositional generalization hard on a particular test instance.

Semantic Parsing

COVR: A test-bed for Visually Grounded Compositional Generalization with real images

1 code implementation EMNLP 2021 Ben Bogin, Shivanshu Gupta, Matt Gardner, Jonathan Berant

Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero- or few-shot setting.

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