Papers by Neha Srikanth
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification (2021.findings-acl)
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| Challenge: | a new study examines the use of content addition in text simplification when complex concepts need to be explained. |
| Approach: | They present a data-driven study of content addition in text simplification . they analyze 1.3K instances of elaborative simplification in the Newsela corpus . |
| Outcome: | The proposed study shows that contextual specificity can improve elaboration generation performance. |
Partial-input baselines show that NLI models can ignore context, but they don’t. (2022.naacl-main)
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| Challenge: | Researchers have shown that many datasets contain statistical biases, or "annotation artifacts" that systems leverage to correctly predict entailment. |
| Approach: | They propose to use edited contexts to examine RoBERTa models' sensitivity to edited context to examine their model's sensitivity. |
| Outcome: | The proposed model can learn to condition on context, despite being trained on artifact-ridden datasets. |
Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering (2024.naacl-long)
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Neha Srikanth, Rupak Sarkar, Heran Mane, Elizabeth Aparicio, Quynh Nguyen, Rachel Rudinger, Jordan Boyd-Graber
| Challenge: | a question-answering system must address pragmatic inferences to answer usefully, says a new study . human information needs are often inferred from the surface form, but answers must address the pragmatic needs of the question. |
| Approach: | They examine assumptions and implications made when mothers ask questions . they find that incorporating these inferences into QA pipelines produces more complete answers . |
| Outcome: | a study shows that incorporating inferences from questions helps to address harmful beliefs . human needs vary when asking questions, but a complete answer can address them . a QA pipeline can be more effective in addressing these needs, the study finds . |
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models (2025.findings-acl)
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Qin Liu, Chao Shang, Ling Liu, Nikolaos Pappas, Jie Ma, Neha Anna John, Srikanth Doss, Lluis Marquez, Miguel Ballesteros, Yassine Benajiba
| Challenge: | LLaVA-7B demonstrated a decline in safety alignment ability on multi-modal inputs compared to its LLM backbone. |
| Approach: | They propose a method to recover alignment ability from LLM backbone while preserving functional capabilities of VLMs. |
| Outcome: | The proposed framework recovers alignment ability that is inherent in the LLM backbone with minimal impact on fluency and linguistic capabilities of pre-trained VLMs. |
Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs (2025.acl-long)
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| Challenge: | a misalignment or misunderstanding can disrupt communication, leading to confusion or conflict. |
| Approach: | They study failures of grounding in Ubuntu IRC datasets to identify misalignments . they find disruptions in conversational flow are driven by a divergence in beliefs . |
| Outcome: | The findings show that misalignment in common ground can disrupt communication . the study also shows that miscommunications can lead to confusion or conflict . |
NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals (2025.naacl-long)
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| Challenge: | Decomposing text into atomic propositions allows for finergrained inspection of text. |
| Approach: | They propose to decompose atomic propositions into atomic sub-problems that models must weigh when solving the overall problem. |
| Outcome: | The proposed method measures the inferential consistency of models and the diversity of examples in benchmark datasets. |
No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions (2025.acl-long)
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| Challenge: | When a question is poorly posed, answerers struggle to converge on dominant interpretations, while models attempt comprehensive coverage by addressing many interpretations simultaneously. |
| Approach: | They propose a computational framework to study poorly-posedness of questions by generating spaces of potential interpretations and computing distributions based on interpretations chosen by answerers in the Reddit question thread. |
| Outcome: | The proposed framework analyzes poorly-posed questions using a set of interpretations chosen by human answerers and large language models. |
SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps (2025.findings-emnlp)
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| Challenge: | SQLSpace is a representation for text-to-SQL examples derived with minimal human intervention. |
| Approach: | They introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples . they demonstrate that SQLSpace enables analysis that would be difficult with raw examples alone . |
| Outcome: | The proposed representations are human-interpretable and generalizable . they are used to evaluate models with a granularity beyond overall accuracy scores . |