Search Results for author: Srini Narayanan

Found 8 papers, 0 papers with code

On Limitations of the Transformer Architecture

no code implementations13 Feb 2024 Binghui Peng, Srini Narayanan, Christos Papadimitriou

What are the root causes of hallucinations in large language models (LLMs)?

UGIF: UI Grounded Instruction Following

no code implementations14 Nov 2022 Sagar Gubbi Venkatesh, Partha Talukdar, Srini Narayanan

We compare the performance of different LLMs including PaLM and GPT-3 and find that the end-to-end task completion rate is 48% for English UI but the performance drops to 32% for other languages.

Instruction Following Navigate +1

MiQA: A Benchmark for Inference on Metaphorical Questions

no code implementations14 Oct 2022 Iulia-Maria Comsa, Julian Martin Eisenschlos, Srini Narayanan

We propose a benchmark to assess the capability of large language models to reason with conventional metaphors.

Real-Time Sign Language Detection using Human Pose Estimation

no code implementations11 Aug 2020 Amit Moryossef, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, Srini Narayanan

We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing.

Optical Flow Estimation Pose Estimation

Stiffness: A New Perspective on Generalization in Neural Networks

no code implementations28 Jan 2019 Stanislav Fort, Paweł Krzysztof Nowak, Stanislaw Jastrzebski, Srini Narayanan

In particular, we study how stiffness depends on 1) class membership, 2) distance between data points in the input space, 3) training iteration, and 4) learning rate.

Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

no code implementations CL 2017 Ekaterina Shutova, Lin Sun, Elkin Dar{\'\i}o Guti{\'e}rrez, Patricia Lichtenstein, Srini Narayanan

We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings.

Constrained Clustering

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