Papers by Balaji Krishnamurthy
LM-CORE: Language Models with Contextually Relevant External Knowledge (2022.findings-naacl)
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| Challenge: | Large pre-trained language models can capture factual knowledge in their parameters but storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of information and resource requirements. |
| Approach: | They propose a framework that provides explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art knowledge-enhanced language models on knowledge probing tasks and can handle knowledge updates. |
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot (2023.emnlp-main)
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| Challenge: | Existing annotated training datasets hinder development of supervised learning models for multimedia content . lack of annotating benchmarks hinders development of models with satisfactory performance . a recent study shows that large language models have zero-shot performance in multimedia understanding . |
| Approach: | They propose to verbalize long videos to generate their descriptions in natural language . they then perform video-understanding tasks on the generated story as opposed to the original video . |
| Outcome: | The proposed method achieves better results than baselines for video understanding. |
TAN-NTM: Topic Attention Networks for Neural Topic Modeling (2021.acl-long)
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| Challenge: | Topic models have been widely used to learn text representations and gain insight into document corpora. |
| Approach: | They propose a framework which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. |
| Outcome: | The proposed model improves on two downstream tasks: document classification and topic guided keyphrase generation. |
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models (2023.findings-emnlp)
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H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy
| Challenge: | Pre-trained language models have a remarkable improvement in generalization capability . however, this leads to prohibitively long training times and a detrimental environmental impact . |
| Approach: | They propose to use submodular optimization to select highly informative subsets of training data to train multiple PTLMs using only fractions of data. |
| Outcome: | The proposed framework achieves 99% of the performance of fully-trained models using only fraction of training data. |
Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning (2025.acl-long)
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| Challenge: | Prior studies have demonstrated that LLMs generate step-by-step rationales, but limited data is available to improve their performance in commercial settings due to copyright and legal issues. |
| Approach: | They propose a trainable framework that tunes a (small) LLM to generate outputs from a pool of diverse rationales that selectively improves the downstream task. |
| Outcome: | The proposed framework outperforms several trainable and prompting baselines on maths problem solving, natural language inference, and commonsense reasoning. |
HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model (2023.findings-acl)
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| Challenge: | Hierarchical Topic Models (HTMs) often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level subjects. |
| Approach: | They propose a Hyperbolic geometry-based Hierarchical Topic Model that incorporates hierarchical information from hyperbolic geometrics to explicitly model hierarchies in topic models. |
| Outcome: | The proposed model is significantly faster and leaves a much smaller memory footprint than the best-performing baseline. |
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer (2022.naacl-main)
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| Challenge: | Pre-trained language models (PTLMs) have been shown to perform well on natural language tasks. |
| Approach: | They propose a commonsense contextualizer conditioned on sentences as input to make it generically usable in tasks involving natural language text. |
| Outcome: | The proposed model improves on existing methods on CSQA, ARC, QASC and OBQA datasets. |
Form2Seq : A Framework for Higher-Order Form Structure Extraction (2020.emnlp-main)
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| Challenge: | Document structure extraction is a widely researched area for decades due to image resolution and poor semantics. |
| Approach: | They propose a sequence-to-sequence framework for document structure extraction using text . they use a text-based framework to classify low-level constituent elements into ten types . |
| Outcome: | The proposed framework outperforms existing methods for document structure extraction on ICDAR 2013 dataset. |
Synthesizing Human Gaze Feedback for Improved NLP Performance (2023.eacl-main)
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| Challenge: | Prior work on eye tracking and NLP reveals that human scanpaths can aid in understanding and performance of NLP models. |
| Approach: | They propose a model for generating human scanpaths over text that approximates meaningful cognitive signals in human gaze patterns. |
| Outcome: | The proposed model can approximate meaningful cognitive signals in human gaze patterns. |