Siva Reddy

Biography

Siva Reddy is an assistant professor at the School of Computer Science and in the Department of Linguistics at McGill University. He completed a postdoc with the Stanford NLP Group in September 2019.

Reddy’s research goal is to enable machines with natural language understanding abilities in order to facilitate applications like question answering and conversational systems. His expertise includes building symbolic (linguistic and induced) and deep learning models for language.

Current Students

Vaibhav Adlakha

PhD - McGill University

Parishad BehnamGhader

Master's Research - McGill University

PhD - McGill University

Collaborating researcher

Gaurav Kamath

PhD - McGill University

Amirhossein Kazemnejad

Master's Research - McGill University

Github

Aditi Khandelwal

PhD - McGill University

Principal supervisor :

PhD - McGill University

Aravind Krishnan

Research Intern - UNIVERSITÄT DES SAARLANDES

Benno Krojer

PhD - McGill University

andreas.madsen@mila.quebec

Zichao Li

PhD - McGill University

Co-supervisor :

Jackie Cheung

Xing Han Lu

PhD - McGill University

PhD - Polytechnique Montréal

Principal supervisor :

Sarath Chandar

PhD - McGill University

Postdoctorate - McGill University

Arkil Patel

PhD - McGill University

Principal supervisor :

Nooshin Soheililangroodi

Research Intern - McGill University

Karolina Ewa Stańczak

Postdoctorate - McGill University

Github

Ada Tur Tur

Research Intern - McGill University

Github

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Ivan Vulić

Collaborating researcher - Cambridge University

Alejandra Zambrano

Research Intern - McGill University

Blog Posts

October 1, 2024

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Universal Adversarial Triggers Are Not Universal

Nicholas Meade

Arkil Patel

2024-04-24

ArXiv (preprint)

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Parishad BehnamGhader

Vaibhav Adlakha

Marius Mosbach

Dzmitry Bahdanau

Nicolas Chapados

2024-04-09

ArXiv (preprint)

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Parishad BehnamGhader

Vaibhav Adlakha

Marius Mosbach

Dzmitry Bahdanau

Nicolas Chapados

Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is… (see more) only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3 popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive learning, we achieve state-of-the-art performance on MTEB among models that train only on publicly available data. Our strong empirical results and extensive analysis demonstrate that LLMs can be effectively transformed into universal text encoders in a parameter-efficient manner without the need for expensive adaptation or synthetic GPT-4 generated data.

2024-04-09

ArXiv (preprint)

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Xing Han Lu

Zdeněk Kasner

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve… (see more) real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

openreview.net

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Xing Han Lu

Zdeněk Kasner

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve… (see more) real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WebLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our benchmark covers a broad range of patterns on over 150 real-world websites and can be used to train and evaluate agents in diverse scenarios. Due to the magnitude of information present, Large Language Models (LLMs) cannot process entire web pages in real-time. To solve this bottleneck, we design a retrieval-inspired model that efficiently prunes HTML pages by ranking relevant elements. We use the selected elements, along with screenshots and action history, to assess a variety of models for their ability to replicate human behavior when navigating the web. Our experiments span from small text-only to proprietary multimodal LLMs. We find that smaller finetuned decoders surpass the best zero-shot LLMs (including GPT-4V), but also larger finetuned multimodal models which were explicitly pretrained on screenshots. However, all finetuned models struggle to generalize to unseen websites. Our findings highlight the need for large multimodal models that can generalize to novel settings. Our code, data and models are available for research: https://mcgill-nlp.github.io/weblinx.

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

openreview.net

A Compositional Typed Semantics for Universal Dependencies

Laurestine Bradford

Timothy John O'donnell

2024-03-02

ArXiv (preprint)

When does word order matter and when doesn't it?

Xuanda Chen

Timothy John O'donnell

Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose … (see more)that linguistic redundancy can explain this phenomenon, whereby word order and other linguistic cues such as case markers provide overlapping and thus redundant information. Our hypothesis is that models exhibit insensitivity to word order when the order provides redundant information, and the degree of insensitivity varies across tasks. We quantify how informative word order is using mutual information (MI) between unscrambled and scrambled sentences. Our results show the effect that the less informative word order is, the more consistent the model's predictions are between unscrambled and scrambled sentences. We also find that the effect varies across tasks: for some tasks, like SST-2, LMs' prediction is almost always consistent with the original one even if the Pointwise-MI (PMI) changes, while for others, like RTE, the consistency is near random when the PMI gets lower, i.e., word order is really important.

2024-02-29

ArXiv (preprint)

The Leukemoid Reaction in Severe Alcoholic Hepatitis: A Case Report

Sachin Agrawal

Sunil Kumar

Sourya Acharya

2024-02-11

Cureus (published)

Data science opportunities of large language models for neuroscience and biomedicine

Danilo Bzdok

Andrew Thieme

Oleksiy Levkovskyy

Paul Wren

Thomas Ray

2024-02-01

Neuron (published)

Benchmarking Vision Language Models for Cultural Understanding

Shravan Nayak

Kanishk Jain

Rabiul Awal

Sjoerd van Steenkiste

Lisa Anne Hendricks

Karolina Sta'nczak

Aishwarya Agrawal

Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of vi… (see more)sual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. This study introduces CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions. Benchmarking VLMs on CulturalVQA, including GPT-4V and Gemini, reveals disparity in their level of cultural understanding across regions, with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too, with clothing, rituals, and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.

2024-01-01

EMNLP (published)

Scope Ambiguities in Large Language Models

Gaurav Kamath

Sebastian Schuster

Sowmya Vajjala

2024-01-01

Trans. Assoc. Comput. Linguistics (published)

StarCoder: may the source be with you!

Raymond Li

Loubna Ben allal

Yangtian Zi

Niklas Muennighoff

Denis Kocetkov

Chenghao Mou

Marc Marone

Christopher Akiki

Jia LI

Jenny Chim

Qian Liu

Evgenii Zheltonozhskii

Terry Yue Zhuo

Thomas Wang

Olivier Dehaene

Mishig Davaadorj

Joel Lamy-Poirier

Joao Monteiro

Oleh Shliazhko

Nicolas Gontier … (see 49 more)

Nicholas Meade

Armel Zebaze

Ming-Ho Yee

Logesh Kumar Umapathi

Jian Zhu

Ben Lipkin

Muhtasham Oblokulov

Zhiruo Wang

Rudra Murthy

Jason T Stillerman

Siva Sankalp Patel

Dmitry Abulkhanov

Marco Zocca

Manan Dey

Zhihan Zhang

N. Fahmy

Urvashi Bhattacharyya

Wenhao Yu

Swayam Singh

Sasha Luccioni

Paulo Villegas

Jan Ebert

M. Kunakov

Fedor Zhdanov

Manuel Romero

Tony Lee

Nadav Timor

Jennifer Ding

Claire S Schlesinger

Hailey Schoelkopf

Jana Ebert

Tri Dao

Mayank Mishra

Alex Gu

Jennifer Robinson

Sean Hughes

Carolyn Jane Anderson

Brendan Dolan-Gavitt

Danish Contractor

Daniel Fried

Dzmitry Bahdanau

Yacine Jernite

Carlos Muñoz Ferrandis

Sean M. Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs)… (see more), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

2023-12-17

TMLR (accepted)