7 citations · 18 across the 10 of their papers we have counts for
11 papers
Leveraging In-Context Learning for Language Model Agents
Shivanshu Gupta, Sameer Singh, Ashish Sabharwal +2
In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to…
LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions
Hadi Askari, Shivanshu Gupta, Fei Wang +2
Pretrained Large Language Models (LLMs) achieve strong performance across a wide range of tasks, yet exhibit substantial variability in the various layers' training quality with re…
Unraveling Indirect In-Context Learning Using Influence Functions
Hadi Askari, Shivanshu Gupta, Terry Tong +3
In this work, we introduce a novel paradigm for generalized In-Context Learning (ICL), termed Indirect In-Context Learning. In Indirect ICL, we explore demonstration selection stra…
GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks
Shivanshu Gupta, Clemens Rosenbaum, Ethan R. Elenberg
In-context Learning (ICL) is the ability of Large Language Models (LLMs) to perform new tasks when conditioned on prompts comprising a few task examples. However, ICL performance c…
Leveraging Code to Improve In-context Learning for Semantic Parsing
Ben Bogin, Shivanshu Gupta, Peter Clark +1
In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization. However, learning to parse to rare domain-specific l…
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha +1
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. In…
Coverage-based Example Selection for In-Context Learning
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 informati…
Successive Prompting for Decomposing Complex Questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh +1
Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of…
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation
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 sche…
Unobserved Local Structures Make Compositional Generalization Hard
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…
COVR: A test-bed for Visually Grounded Compositional Generalization with real images
Ben Bogin, Shivanshu Gupta, Matt Gardner +1
While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to syn…