Search Results for author: Nitish Gupta

Found 25 papers, 10 papers with code

IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages

1 code implementation25 Apr 2024 Harman Singh, Nitish Gupta, Shikhar Bharadwaj, Dinesh Tewari, Partha Talukdar

To facilitate research on multilingual LLM evaluation, we release IndicGenBench - the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set 29 of Indic languages covering 13 scripts and 4 language families.

Cross-Lingual Question Answering Diversity +1

Potential-Based Reward Shaping For Intrinsic Motivation

no code implementations12 Feb 2024 Grant C. Forbes, Nitish Gupta, Leonardo Villalobos-Arias, Colin M. Potts, Arnav Jhala, David L. Roberts

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments.

LLM Augmented LLMs: Expanding Capabilities through Composition

1 code implementation4 Jan 2024 Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar

Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains.

Arithmetic Reasoning Code Generation

Bootstrapping Multilingual Semantic Parsers using Large Language Models

no code implementations13 Oct 2022 Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta, Shachi Dave, Sunita Sarawagi, Partha Talukdar

Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.

Semantic Parsing Translation

QA Is the New KR: Question-Answer Pairs as Knowledge Bases

no code implementations1 Jul 2022 Wenhu Chen, William W. Cohen, Michiel de Jong, Nitish Gupta, Alessandro Presta, Pat Verga, John Wieting

In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking.

Entity Linking Position +2

Event Linking: Grounding Event Mentions to Wikipedia

1 code implementation15 Dec 2021 Xiaodong Yu, Wenpeng Yin, Nitish Gupta, Dan Roth

Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task.

Entity Linking Natural Language Understanding

Study Of German Manufacturing Firms: Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance

no code implementations16 Aug 2021 Nitish Gupta, Ruchir Kaul, Satwik Gupta, Jay Shah

The results based on the nonparametric nearest neighbor matching suggest a statistically significant positive effect of the EU ETS on the economic performance of the regulated firms during Phase I of the EU ETS.

Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance: A Study Of German Manufacturing Firms

no code implementations16 Aug 2021 Nitish Gupta, Jay Shah, Satwik Gupta, Ruchir Kaul

In this paper, we estimate the causal impact (i. e. Average Treatment Effect, ATT) of the EU ETS on GHG emissions and firm competitiveness (primarily measured by employment, turnover, and exports levels) by combining a difference-in-differences approach with semi-parametric matching techniques and estimators an to investigate the effect of the EU ETS on the economic performance of these German manufacturing firms using a Stochastic Production Frontier model.

Enforcing Consistency in Weakly Supervised Semantic Parsing

1 code implementation ACL 2021 Nitish Gupta, Sameer Singh, Matt Gardner

The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons.

Semantic Parsing Visual Reasoning

Paired Examples as Indirect Supervision in Latent Decision Models

no code implementations EMNLP 2021 Nitish Gupta, Sameer Singh, Matt Gardner, Dan Roth

Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.

Out-of-Distribution Generalization Question Answering +2

What do we expect from Multiple-choice QA Systems?

no code implementations Findings of the Association for Computational Linguistics 2020 Krunal Shah, Nitish Gupta, Dan Roth

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.

Multiple-choice Multiple Choice Question Answering (MCQA)

Evaluating NLP Models via Contrast Sets

no code implementations1 Oct 2020 Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, A. Zhang, Ben Zhou

Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.

Reading Comprehension Sentiment Analysis

Overestimation of Syntactic Representation in Neural Language Models

no code implementations ACL 2020 Jordan Kodner, Nitish Gupta

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.

Obtaining Faithful Interpretations from Compositional Neural Networks

1 code implementation ACL 2020 Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner

Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture.

Overestimation of Syntactic Representationin Neural Language Models

no code implementations10 Apr 2020 Jordan Kodner, Nitish Gupta

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.

Robust Named Entity Recognition with Truecasing Pretraining

no code implementations15 Dec 2019 Stephen Mayhew, Nitish Gupta, Dan Roth

Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.

named-entity-recognition Named Entity Recognition +1

Neural Module Networks for Reasoning over Text

2 code implementations ICLR 2020 Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, Matt Gardner

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations.

Diversity Inductive Bias

Joint Multilingual Supervision for Cross-lingual Entity Linking

1 code implementation EMNLP 2018 Shyam Upadhyay, Nitish Gupta, Dan Roth

This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.

Cross-Lingual Entity Linking Entity Linking

Entity Linking via Joint Encoding of Types, Descriptions, and Context

no code implementations EMNLP 2017 Nitish Gupta, Sameer Singh, Dan Roth

For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge.

Entity Linking

Collectively Embedding Multi-Relational Data for Predicting User Preferences

no code implementations23 Apr 2015 Nitish Gupta, Sameer Singh

Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations.

Attribute Collaborative Filtering

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