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Ganesh Ramakrishnan

27 accepted papers

2026

Minibatch selection for Language Models via Partition Matroid Constrained Gradient Matching

ICML 2026poster

Training Large Language Models (LLMs) on heterogeneous datasets requires optimizing domain representations to balance convergence speed and domain coverage. While recent methods reduce computational overhead by selecting high-quality data subsets, they typically perform selection independently per d…

Cited by 0SourceScholar
2025

ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification

NAACL 2025findings

We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of…

Cited by 0SourcePDFScholar
2025

Bandit Guided Submodular Curriculum for Adaptive Subset Selection

NeurIPS 2025poster

Traditional curriculum learning proceeds from easy to hard samples, yet defining a reliable notion of difficulty remains elusive. Prior work has used submodular functions to induce difficulty scores in curriculum learning. We reinterpret adaptive subset selection and formulate it as a multi-armed ba…

Cited by 0SourcecodeScholar
2025

GuideQ: Framework for Guided Questioning for progressive informational collection and classification

NAACL 2025findings

The veracity of a factoid is largely independent of the language it is written in. However, language models are inconsistent in their ability to answer the same factual question across languages. This raises questions about how LLMs represent a given fact across languages. We explore multilingual fa…

2025

INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages

NAACL 2025findings

Large Language Models (LLMs) perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. To bridge this gap, we introduce the Indic-QA Benchmark, a large dataset for context-grounded question answering in 11 maj…

2025

LexGen: Domain-aware Multilingual Lexicon Generation

ACL 2025long

Lexicon or dictionary generation across domains has the potential for societal impact, as it can potentially enhance information accessibility for a diverse user base while preserving language identity. Prior work in the field primarily focuses on bilingual lexical induction, which deals with word a…

2024

Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models

EMNLP 2024finding

In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition (ASR), where proper nouns in an utterance may originate from a language different from the language in which the ASR system is trained. We enhance the performance of end-to-end ASR systems…

Cited by 0SourcePDFScholar
2024

DictDis: Dictionary Constrained Disambiguation for Improved NMT

EMNLP 2024finding

Domain-specific neural machine translation (NMT) systems (, in educational applications) are socially significant with the potential to help make information accessible to a diverse set of users in multilingual societies. Such NMT systems should be lexically constrained and draw from domain-specific…

2024

Few shot chain-of-thought driven reasoning to prompt LLMs for open-ended medical question answering

EMNLP 2024finding

In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) rea…

2024

SMART: Submodular Data Mixture Strategy for Instruction Tuning

ACL 2024findings

Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task proportions during finetuning, but finding the right balance…

2024

Samayik: A Benchmark and Dataset for English-Sanskrit Translation

COLING 2024main

We release Saamayik, a dataset of around 53,000 parallel English-Sanskrit sentences, written in contemporary prose. Sanskrit is a classical language still in sustenance and has a rich documented heritage. However, due to the limited availability of digitized content, it still remains a low-resource…

2023

Adaptive Mixing of Auxiliary Losses in Supervised Learning

AAAI 2023technical

In many supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective. For instance, knowledge distillation aims to mimic outputs of a powerful teacher model; similarly, in rule-based approaches, weak label…

2023

DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation

ACL 2023long

State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent, a commonly adopted solution is to finetune the ASR model using accent-specific labeled speech. However, acquiring larg…

Cited by 8SourcePDFScholar
2023

Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models

ICML 2023poster

We study a new framework of learning mixture models via automatic clustering called PRESTO, wherein we optimize a joint objective function on the model parameters and the partitioning, with each model tailored to perform well on its specific cluster. In contrast to prior work, we do not assume any g…

Cited by 0SourcePDFScholar
2023

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models

EMNLP 2023long findings

A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size. Consequently, we are witnessing the development of enormous models pushing th…

Cited by 0SourcecodeScholar
2023

Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration

IJCAI 2023poster

The problem of audio-to-text alignment has seen significant amount of research using complete supervision during training. However, this is typically not in the context of long audio recordings wherein the text being queried does not appear verbatim within the audio file. This work is a collaboratio…

2023

When Do Neural Nets Outperform Boosted Trees on Tabular Data?

NeurIPS 2023poster

Tabular data is one of the most commonly used types of data in machine learning. Despite recent advances in neural nets (NNs) for tabular data, there is still an active discussion on whether or not NNs generally outperform gradient-boosted decision trees (GBDTs) on tabular data, with several recent…

2022

A Benchmark and Dataset for Post-OCR text correction in Sanskrit

EMNLP 2022finding

Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scanned-image forms. However, it is still considered to be a low-resource language when it comes to available digital resources. In this work, we release a post-OCR text c…

2022

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

NeurIPS 2022accept

Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. In addition, finding the optimal hyper-parameter configuration, even with state-of-the-art (SOTA) hyper-parameter optimiz…

2022

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

ACL 2022findings

A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain. Although a small amount of labeled data cannot be used to train a model, it can be used effectively for the generation of humaninterpretable labeling fu…

2022

PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection

AAAI 2022technical

With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of…

2022

Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training

EMNLP 2022finding

Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost. Training with a subset of training data could mitigate this problem if the subset selected could achieve on-par performance with training with the entire dataset. Although there are many…

2022

WARM: A Weakly (+Semi) Supervised Math Word Problem Solver

COLING 2022main

Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solving MWPs require full supervision in the form of intermediate equations. However, labeling every MWP with its corresponding equations is a time-consuming and expensive…

2021

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

AAAI 2021technical

Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (with hyperparameter tuning) requires significant computing resources and time. Secondly, real-world data is noisy and imb…

2021

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

ICML 2021spotlight

The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framewo…

2021

Training Data Subset Selection for Regression with Controlled Generalization Error

ICML 2021spotlight

Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a smaller subset may show poor generalization ability. In this paper, our goal is to design an algorithm for selecting a sub…