Papers by Rudra Murthy
Prompting with Pseudo-Code Instructions (2023.emnlp-main)
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| Challenge: | ambiguity in natural language can hinder performance of large language models. |
| Approach: | They manually create a dataset of pseudo-code prompts for 132 different classification, QA, and generative language tasks, sourced from the Super-NaturalInstructions dataset. |
| Outcome: | The pseudo-code prompts improve the performance of two LLM families, BLOOM and CodeGen. |
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5 (2025.naacl-long)
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| Challenge: | Existing benchmarks for evaluating retrieval models in Hindi are lacking . despite efforts to build multilingual retrieval systems, this is still a work in progress . |
| Approach: | They evaluate Hindi retrieval models on the Hindi-BEIR benchmark and introduce a multilingual model that leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
| Outcome: | The proposed model leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages (2021.emnlp-main)
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| Challenge: | A study of multilingual fine-tuning yields better performance on downstream NLP applications . low resource languages such as Oriya and Punjabi are found to be the largest beneficiaries of multi-lingual fine tuning. |
| Approach: | They propose to leverage the relatedness of languages that belong to the same family in NLP models by multilingual fine-tuning. |
| Outcome: | The proposed approach improves performance on downstream NLP tasks by 15% compared to monolingual fine-tuning. |
MILU: A Multi-task Indic Language Understanding Benchmark (2025.naacl-long)
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| Challenge: | Existing benchmarks focus on English, leaving substantial gaps in assessing LLM capabilities in low-resource and linguistically diverse languages. |
| Approach: | They propose a multi-task indic language understanding benchmark to assess LLMs in low-resource languages. |
| Outcome: | The new benchmark spans 8 domains and 41 subjects across 11 Indic languages, reflecting general and culturally specific knowledge. |
On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish (2022.findings-emnlp)
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| Challenge: | Intuitively, Hindi and English corpora should aid improve task performance on code-switched Hindi-English. |
| Approach: | They propose a meta-learning framework that utilizes the labelled resources of the downstream tasks in the constituent languages to improve task performance. |
| Outcome: | The proposed framework improves the performance on downstream tasks on code-switched Hindi-English. |
Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages (2023.acl-long)
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Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, Anoop Kunchukuttan
| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They present the largest publicly available Named Entity Recognition dataset for the 11 major Indian languages from two language families. |
| Outcome: | The proposed dataset is the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. |
Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
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| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
| Approach: | This tutorial examines approaches to expand the language coverage of LLMs . they look at tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc. |
| Outcome: | This tutorial examines approaches to expand the language coverage of LLMs . it provides an efficient and viable path to bring LLM technologies to low-resource languages . |
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages (N19-1)
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| Challenge: | Existing studies show that transfer learning works best when the languages are related. |
| Approach: | They propose to pre-order assisting language sentences to match the word order of the source language and train the parent model. |
| Outcome: | The proposed model can improve translation quality in low-resource scenarios by pre-ordering the assisting language sentences to match the word order of the source language and training the parent model. |
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing (2020.findings-emnlp)
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| Challenge: | Noun compound interpretation is the task of uncovering the semantic relation between the components of a noun compound. |
| Approach: | They propose an unsupervised method for identifying such relations between the components of a noun compound using pre-trained contextualized language models. |
| Outcome: | The proposed method outperforms supervised approaches for free paraphrasing and prepositional paraphrases using pre-trained language models to uncover ‘missing’ words. |
Power doesn’t reside in size: A Low Parameter Hybrid Language Model (HLM) for Sentiment Analysis in Code-mixed data (2025.emnlp-main)
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Pavan Sai Balaga, Nagasamudram Karthik, Challa Vishwanath, Raksha Sharma, Rudra Murthy, Ashish Mittal
| Challenge: | Code-mixed text presents significant challenges for machine learning due to interplay of distinct grammatical structures, effectively forming a hybrid language. |
| Approach: | They propose a Hybrid Language Model that combines a multilingual encoder and a lightweight decoder to achieve sentiment classification performance comparable to those of fine-tuned Large Language Models. |
| Outcome: | The proposed model outperforms models trained individually in sentiment detection tasks. |
UR2N: Unified Retriever and ReraNker (2025.coling-industry)
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| Challenge: | XTR-style retrieval on top of trained Mono-T5 reranker is suboptimal for two-stage retrieval, arguing that it is sub-optimal. |
| Approach: | They propose a unified encoder-decoder architecture with a novel training regimen which enables the encoder representation to be used for retrieval and the decoder for re-ranking within a single unified model. |
| Outcome: | The proposed architecture outperforms ColBERT, XTR, and even serves as a superior reranker compared to the Mono-T5 re-ranker. |
HiNER: A large Hindi Named Entity Recognition Dataset (2022.lrec-1)
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Rudra Murthy, Pallab Bhattacharjee, Rahul Sharnagat, Jyotsana Khatri, Diptesh Kanojia, Pushpak Bhattacharyya
| Challenge: | Named Entity Recognition (NER) is a lowerlevel task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. |
| Approach: | They propose to use a standard-abiding Hindi NER dataset to analyze the annotations of a class of naming entities in free text. |
| Outcome: | The proposed dataset achieves a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set. |
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour (2020.aacl-main)
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| Challenge: | Using gaze behaviour to solve automatic essay grading tasks is costly in terms of time and money. |
| Approach: | They propose to collect gaze behaviour from 48 essays and learn gaze behaviour for the rest of the essays using a multi-task learning framework. |
| Outcome: | The proposed approach achieves a statistically significant improvement over the state-of-the-art system for the essay sets where gaze data is available. |
“You are Beautiful, Body Image Stereotypes are Ugly!” BIStereo: A Benchmark to Measure Body Image Stereotypes in Language Models (2025.findings-acl)
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| Challenge: | BIStereo is a suite of language models that uncover body image stereotypes in language models. |
| Approach: | They propose a metric, TriSentBias, that captures the biased preferences of LMs towards a certain body type over others. |
| Outcome: | The proposed metric captures biased preferences of LMs towards a certain body type over others. |
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs’ Pragmatics Capabilities (2024.findings-acl)
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| Challenge: | Pragmatics understanding is not well studied in LLMs, but their understanding of pragmatics is lacking. |
| Approach: | They propose to use a dataset to measure LLMs' understanding of pragmatics to evaluate their models. |
| Outcome: | The proposed dataset includes 14 tasks in four pragmatics phenomena, namely; Implicature, Presupposition, Reference, and Deixis. |
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER (P18-2)
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| Challenge: | Existing approaches to improve NER performance add training data from one or more assisting languages to the primary language. |
| Approach: | They propose a metric based on symmetric KL divergence to filter out highly divergent training instances in the assisting language. |
| Outcome: | The proposed method improves NER performance in many languages, including those with limited training data. |
Semi-Structured Object Sequence Encoders (2023.findings-emnlp)
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Rudra Murthy, Riyaz Bhat, Chulaka Gunasekara, Siva Patel, Hui Wan, Tejas Dhamecha, Danish Contractor, Marina Danilevsky
| Challenge: | Semi-structured object sequences are often represented as a sequence of key-value pairs over time . authors propose a two-part approach that takes each key independently and encodes a representation of its values over time. |
| Approach: | They propose a two-part approach that first considers each key independently and encodes a representation of its values over time. |
| Outcome: | The proposed approach outperforms existing methods on multiple prediction tasks using real-world data. |
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages (2025.findings-naacl)
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Abhishek Kumar Singh, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen, Ashish Mittal, Ganesh Ramakrishnan
| Challenge: | Large Language Models perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. |
| Approach: | They propose to release a large dataset for context-grounded question answering in 11 major Indian languages. |
| Outcome: | The Indic-QA Benchmark compared large datasets of large LLMs on extractive and abstractive tasks in 11 major Indian languages. |
Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach (2025.findings-acl)
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| Challenge: | a new study addresses bias and stereotypes in language models by exploring how learning them together improves performance. |
| Approach: | They propose a dataset for bias and stereotype detection that integrates religion, gender, socio-economic status, race, profession, and others. |
| Outcome: | The proposed dataset compares encoder-only models and fine-tuned decoder- only models . the results show that learning stereotypes together improves bias detection . |