Search Results for author: Ganesh Jawahar

Found 14 papers, 8 papers with code

LLM Performance Predictors are good initializers for Architecture Search

no code implementations25 Oct 2023 Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Dujian Ding

We show that HS-NAS performs very similar to SOTA NAS across benchmarks, reduces search hours by 50% roughly, and in some cases, improves latency, GFLOPs, and model size.

Machine Translation Neural Architecture Search

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

6 code implementations5 Jun 2023 Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah

To address these challenges, we develop Orca (We are working with our legal team to publicly release a diff of the model weights in accordance with LLaMA's release policy to be published at https://aka. ms/orca-lm), a 13-billion parameter model that learns to imitate the reasoning process of LFMs.

Imitation Learning Knowledge Distillation

Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints

no code implementations6 Oct 2022 Ganesh Jawahar, Subhabrata Mukherjee, Debadeepta Dey, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Caio Cesar Teodoro Mendes, Gustavo Henrique de Rosa, Shital Shah

In this work, we study the more challenging open-domain setting consisting of low frequency user prompt patterns (or broad prompts, e. g., prompt about 93rd academy awards) and demonstrate the effectiveness of character-based language models.

Inductive Bias

Automatic Detection of Entity-Manipulated Text using Factual Knowledge

1 code implementation ACL 2022 Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan

We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article.

Contrastive Learning of Sociopragmatic Meaning in Social Media

1 code implementation15 Mar 2022 Chiyu Zhang, Muhammad Abdul-Mageed, Ganesh Jawahar

Recent progress in representation and contrastive learning in NLP has not widely considered the class of \textit{sociopragmatic meaning} (i. e., meaning in interaction within different language communities).

Contrastive Learning

Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora

1 code implementation ACL 2020 Hila Gonen, Ganesh Jawahar, Djamé Seddah, Yoav Goldberg

The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science.

Word Embeddings

Automatic Detection of Machine Generated Text: A Critical Survey

1 code implementation COLING 2020 Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan

Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs.

Contextualized Diachronic Word Representations

1 code implementation WS 2019 Ganesh Jawahar, Djam{\'e} Seddah

We devise a novel attentional model, based on Bernoulli word embeddings, that are conditioned on contextual extra-linguistic (social) features such as network, spatial and socio-economic variables, which are associated with Twitter users, as well as topic-based features.

Diachronic Word Embeddings Inductive Bias +1

What Does BERT Learn about the Structure of Language?

1 code implementation ACL 2019 Ganesh Jawahar, Beno{\^\i}t Sagot, Djam{\'e} Seddah

BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks.

ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing

no code implementations CONLL 2018 Ganesh Jawahar, Benjamin Muller, Amal Fethi, Louis Martin, {\'E}ric Villemonte de la Clergerie, Beno{\^\i}t Sagot, Djam{\'e} Seddah

We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set.

Dependency Parsing Language Modelling

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