Siva Reddy

vaibhav.adlakha@mila.quebec

Biographie

Siva Reddy est professeur adjoint en informatique et linguistique à l’Université McGill. Ses travaux portent sur les algorithmes qui permettent aux ordinateurs de comprendre et de traiter les langues humaines. Il a fait ses études postdoctorales avec le Stanford NLP Group. Son expertise inclut la construction de symboliques linguistiques et induites et de modèles d’apprentissage profond pour le langage.

Étudiants actuels

Vaibhav Adlakha

Doctorat - McGill

Kareem Ahmed

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

kareem.yousrii@gmail.com

Parishad BehnamGhader

Doctorat - McGill

parishad.behnamghader@mila.quebec

Doctorat - McGill

mehar.bhatia@mila.quebec

verna.dankers@mila.quebec

Verna Dankers

Postdoctorat - McGill

Visiteur de recherche indépendant

desmond.elliott@mila.quebec

spandana.gella@mila.quebec

Jay Gala

Maîtrise recherche - McGill

Co-superviseur⋅e :

Collaborateur·rice de recherche

gaurav.kamath@mila.quebec

Gaurav Kamath

Doctorat - McGill

Co-superviseur⋅e :

Timothy O'Donnell

Aditi Khandelwal

Doctorat - McGill

Superviseur⋅e principal⋅e :

Golnoosh Farnadi

aditi.khandelwal@mila.quebec

Austin Kraft

Doctorat - McGill

Co-superviseur⋅e :

Timothy O'Donnell

austin.kraft@mila.quebec

Benno Krojer

Doctorat - McGill

benno.krojer@mila.quebec

Zichao Li

Doctorat - McGill

Co-superviseur⋅e :

Jackie Cheung

zichao.li@mila.quebec

Fengyuan Liu

Maîtrise recherche - McGill

Co-superviseur⋅e :

Dzmitry Bahdanau

fengyuan.liu@mila.quebec

sreenath.madathil@mila.quebec

Sreenath Madathil

Maîtrise recherche - McGill

nicholas.meade@mila.quebec

Nicholas Meade

Doctorat - McGill

Postdoctorat - McGill

marius.mosbach@mila.quebec

Marzia Nouri

Maîtrise recherche - McGill

marzia.nouri@mila.quebec

Arkil Patel

Doctorat - McGill

Superviseur⋅e principal⋅e :

Dzmitry Bahdanau

arkil.patel@mila.quebec

erfan.raoofian@mila.quebec

Neel Rajani

Collaborateur·rice de recherche - University of Edinburgh

neel.rajani@mila.quebec

Collaborateur·rice de recherche - N/A

Ben Saine

Collaborateur·rice de recherche - McGill

ben.saine@mila.quebec

Kusha Sareen

Doctorat - McGill

kusha.sareen@mila.quebec

dongchan.shin@mila.quebec

Dongchan Shin

Collaborateur·rice alumni

tomasvergara.browne@mila.quebec

Ada Tur

Stagiaire de recherche - McGill

Doctorat - McGill

Collaborateur·rice de recherche - McGill

donghao.zeng@mila.quebec

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

Billets de blogue

1 octobre 2024

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

Learning Action and Reasoning-Centric Image Editing from Videos and Simulations

Varun Jampani

Christopher Pal

2024-12-10

Neural Information Processing Systems (Accept (Spotlight))

arxiv.org

VinePPO: Accurate Credit Assignment in RL for LLM Mathematical Reasoning

Amirhossein Kazemnejad

Nicolas Roux

Large language models (LLMs) are increasingly required to solve complex reasoning tasks, like mathematical problems, that involve multiple r… (voir plus)easoning steps before feedback is received. Effectively identifying and prioritizing key steps by accurately assigning credit to these intermediate steps is essential for enhancing model performance. Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm for finetuning LLMs, addresses the credit assignment problem by employing value networks to predict the expected cumulative rewards of intermediate states. In this work, we identify significant limitations with this value estimation method. To address this, we propose \methodname that leverages the flexibility of language environments to compute unbiased Monte Carlo-based estimates of the intermediate values. VinePPO consistently outperforms standard PPO, doing so more efficiently and with lower divergence from the reference model. Our findings underscore the critical importance of accurate credit assignment in LLM post-training and present a simple, yet effective solution.

2024-10-08

NeurIPS.cc/2024/Workshop/MATH-AI (accepté)

openreview.net

Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

Varun Jampani

Christopher Pal

2024-09-25

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)

openreview.net

Are self-explanations from Large Language Models faithful?

Andreas Madsen

A. Chandar

2024-07-31

Findings of the Association for Computational Linguistics ACL 2024 (publié)

arxiv.org

Faithfulness Measurable Masked Language Models

Andreas Madsen

Sarath Chandar

A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortuna… (voir plus)tely, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues, and existing solutions that address this are computationally expensive and employ proxy models. Furthermore, other metrics are very limited in scope. This work proposes an inherently faithfulness measurable model that addresses these challenges. This is achieved using a novel fine-tuning method that incorporates masking, such that masking tokens become in-distribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to 16 different datasets and validate it using statistical in-distribution tests. The faithfulness is then measured with 9 different importance measures. Because masking is in-distribution, importance measures that themselves use masking become consistently more faithful. Additionally, because the model makes faithfulness cheap to measure, we can optimize explanations towards maximal faithfulness; thus, our model becomes indirectly inherently explainable.

2024-07-22

International Conference on Machine Learning (Accept (Spotlight))

proceedings.mlr.press

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… (voir plus) 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

2024-07-22

ICML (Accept (Spotlight))

proceedings.mlr.press

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Parishad BehnamGhader

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… (voir plus) 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 4 popular LLMs ranging from 1.3B to 8B 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 (as of May 24, 2024). 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.

2024-07-09

colmweb.org/COLM/2024/Conference (accepté)

openreview.net

Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Vaibhav Adlakha

Parishad BehnamGhader

Xing Han Lu

Nicholas Meade

Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as … (voir plus)question answering (QA). By simply prepending retrieved documents in its input along with an instruction, these models can be adapted to various information domains and tasks without additional fine-tuning. While the model responses tend to be natural and fluent, the additional verbosity makes traditional QA evaluation metrics such as exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we investigate the performance of instruction-following models across three information-seeking QA tasks. We use both automatic and human evaluation to evaluate these models along two dimensions: 1) how well they satisfy the user's information need (correctness), and 2) whether they produce a response based on the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness. We then propose simple token-overlap based and model-based metrics that reflect the true performance of these models. Our analysis reveals that instruction-following models are competitive, and sometimes even outperform fine-tuned models for correctness. However, these models struggle to stick to the provided knowledge and often hallucinate in their responses. We hope our work encourages a more holistic evaluation of instruction-following models for QA. Our code and data is available at https://github.com/McGill-NLP/instruct-qa

2024-05-15

Transactions of the Association for Computational Linguistics (publié)

arxiv.org

Interpretability Needs a New Paradigm

Andreas Madsen

Himabindu Lakkaraju

A. Chandar

2024-05-07

ArXiv (prépublication)