Papers by Venkatesh Saligrama
Hearing Between the Lines: Unlocking the Reasoning Power of LLMs for Speech Evaluation (2026.findings-eacl)
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| Challenge: | Large Language Model (LLM) judges are limited to textual content, resulting in expensive and opaque evaluation methods. |
| Approach: | They propose a framework that enables large language model judges to reason over audio cues . they introduce a human chain-of-thought annotation protocol to improve judge diagnostic capability . |
| Outcome: | The proposed framework achieves higher agreement with human raters than ALMs and transcript-only LLM judges while being significantly more cost-effective. |
Ideology Prediction from Scarce and Biased Supervision: Learn to Disregard the “What” and Focus on the “How”! (2023.acl-long)
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| Challenge: | a novel supervised learning approach for political ideology prediction is needed for many applications. |
| Approach: | They propose a supervised learning approach for political ideology prediction that decomposes document embeddings into a linear superposition of two vectors. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets with biased data with 5% accuracy. |
DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality (2026.acl-long)
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Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov, Bhuwan Dhingra, Markus Dreyer, Venkatesh Saligrama
| Challenge: | Existing fact-checkers usually target general-domain atomic claims . citation-grounded fact- checking ignores claims without explicit citations . |
| Approach: | They propose to use a benchmark to test whether claim-level factuality is transferable . they instantiate **Audit-then-Score** as a versioned DRR factualism benchmark . |
| Outcome: | The proposed benchmark outperforms the best prior deep-research and traditional fact-checkers by 14.3 and 24.9 points. |
Robust Text Classifier on Test-Time Budgets (D19-1)
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| Challenge: | Recent advances in deep neural networks (DNNs) achieve high accuracy on many text classification tasks. |
| Approach: | They propose a generic framework for learning a robust text classification model . they use a data aggregation method to train the classifier on a large corpus of text . |
| Outcome: | The proposed framework achieves consistent speedup with little degradation in accuracy on four benchmark text classification tasks. |
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging (2025.emnlp-main)
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Aoming Liu, Kevin Miller, Venkatesh Saligrama, Kate Saenko, Boqing Gong, Ser-Nam Lim, Bryan A. Plummer
| Challenge: | Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. |
| Approach: | They propose a framework that updates the entire model using weight averaging to maximize generalization potential while minimizing computational costs. |
| Outcome: | The proposed framework outperforms previous methods by up to 69% while being up to 60x more efficient. |