Papers by Raghuveer Thirukovalluru
Atomic Self-Consistency for Better Long Form Generations (2024.emnlp-main)
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| Challenge: | Recent work has aimed to improve LLM generations by filtering out hallucinations, thereby improving the accuracy of the information in responses. |
| Approach: | They propose a technique that improves the recall of relevant information in an LLM. |
| Outcome: | The proposed technique improves the recall of relevant information in an LLM. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings (2025.findings-naacl)
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| Challenge: | Training-free embedding methods focus on optimizing embeddable prompts . previous methods have overlooked the benefits of utilizing generative abilities of LLMs - GenEOL . |
| Approach: | They propose a method that leverages pretrained large language models to embed text . they propose generating diverse transformations of a sentence that preserve its meaning . |
| Outcome: | The proposed method outperforms existing training-free embedding methods by 2.85 points on the sentence semantic text similarity (STS) benchmark. |
Scaling Within Document Coreference to Long Texts (2021.findings-acl)
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Raghuveer Thirukovalluru, Nicholas Monath, Kumar Shridhar, Manzil Zaheer, Mrinmaya Sachan, Andrew McCallum
| Challenge: | Existing end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. |
| Approach: | They propose an approximation to end-to-end coreference resolution models which scales gracefully to documents of any length. |
| Outcome: | The proposed model reduces training and inference time and memory costs compared to current models with minimal loss in accuracy. |
Longtonotes: OntoNotes with Longer Coreference Chains (2023.findings-eacl)
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Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan
| Challenge: | Using Ontonotes, documents in certain genres were split into smaller parts for ease of annotation. |
| Approach: | They propose to merge annotations from documents split into smaller parts in Ontonotes for ease of annotation. |
| Outcome: | The proposed corpus restores documents to their original form, revealing dramatic increases in length in certain genres. |
Calibrating Long-form Generations From Large Language Models (2024.findings-emnlp)
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| Challenge: | Conventional calibration methods treat answer correctness as binary and do not work for long-form generation where an answer can be partially correct. |
| Approach: | They propose a framework where correctness of LLMs' responses and associated confidence levels are treated as distributions across a range of scores. |
| Outcome: | The proposed framework treats the correctness of the LLMs’ responses and their associated confidence levels as distributions across a range of scores. |
Sequence Reducible Holdout Loss for Language Model Pretraining (2024.lrec-main)
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| Challenge: | Data selection techniques have shown empirical benefits in reducing the number of gradient steps to train neural models. |
| Approach: | They propose to modify an existing data selection technique to adapt it to the sequence losses typical in language modeling. |
| Outcome: | The proposed technique reduces the number of steps required to train neural models by 4.3% and improves generalization ability on out of domain datasets. |