Papers by Priyanka Agrawal
Dolomites: Domain-Specific Long-Form Methodical Tasks (2025.tacl-1)
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Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, Pranesh Srinivasan, Fantine Huot, Jonathan Berant, Mark Yatskar, Dipanjan Das, Mirella Lapata, Chris Alberti
| Challenge: | Experts in various fields perform methodical writing tasks to plan, organize, and report their work. |
| Approach: | They propose a benchmark with specifications for 519 methodical writing tasks . they use expert revisions of up to 10 model-generated examples to evaluate contemporary language models. |
| Outcome: | The proposed benchmark includes specifications for 519 methodical writing tasks . it includes examples with input and output examples, and is available at https://dolomites-benchmark.github.io/ . |
Benchmarking Large Language Model Capabilities for Conditional Generation (2023.acl-long)
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| Challenge: | Autoregressive and pre-trained large language models have shifted the field from application-specific to generation-based approaches. |
| Approach: | They propose to adapt existing application-specific generation benchmarks to pre-trained large language models to better suit different tasks. |
| Outcome: | The proposed models differ in their applicability to different data regimes and their generalization to multiple languages. |
Unified Semantic Parsing with Weak Supervision (P19-1)
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Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
| Challenge: | Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs. |
| Approach: | They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision. |
| Outcome: | The proposed model improves performance by 20% on the Overnight dataset. |
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels (2025.acl-long)
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| Challenge: | Recent studies indicate that finer sentence or span-level labels provide more accurate and interpretable feedback for LLM optimization. |
| Approach: | They propose a suite of models to disaggregate response-level labels into sentence-level (pseudo-)labels through Multiple Instance Learning and Learning from Label Proportions (LLP) formulations. |
| Outcome: | The proposed model can reach 93% of the performance of the fully supervised baseline while requiring only around 10% of the gold labels. |
𝜇PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge (2024.eacl-long)
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Fantine Huot, Joshua Maynez, Chris Alberti, Reinald Kim Amplayo, Priyanka Agrawal, Constanza Fierro, Shashi Narayan, Mirella Lapata
| Challenge: | Recent advances in abstractive summarization have focused on English, but more recently, with the advent of large pre-trained models, the task is becoming more complex. |
| Approach: | They propose an approach to cross-lingual summarization that uses an intermediate planning step as a cross-linguistic bridge. |
| Outcome: | The proposed approach achieves state-of-the-art in terms of informativeness and faithfulness on the XWikis dataset. |