Papers by Vaishnavi Vaishnavi
Knowledge Base Question Answering through Recursive Hypergraphs (2021.eacl-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) do not explicitly incorporate the recursive relational group structure in the given knowledge base. |
| Approach: | They propose a method to model KBs through recursive hypergraphs using hypergraph data. |
| Outcome: | The proposed method is based on recursive hypergraphs and has been released on multiple benchmarks. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs (2025.naacl-long)
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Anirudh Phukan, Divyansh Divyansh, Harshit Kumar Morj, Vaishnavi Vaishnavi, Apoorv Saxena, Koustava Goswami
| Challenge: | Large Multimodal Models are plagued by hallucinations that limit their reliability and adoption. |
| Approach: | They propose a method that leverages contextual token embeddings from LMMs to detect hallucinations. |
| Outcome: | The proposed method improves hallucination detection and grounding across diverse categories while excelling in tasks requiring contextual understanding. |
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (2022.naacl-main)
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Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
| Challenge: | Currently, global models are not able to produce personalized responses for individual users, based on their data. |
| Approach: | They propose a scheme for training a single shared model for all users by prepending a fixed, user-specific non-trainable string to each user’s input text. |
| Outcome: | The proposed method outperforms the state-of-the-art model on a suite of sentiment analysis datasets by up to 13 points. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation (2025.findings-naacl)
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| Challenge: | Assertions have been the de facto collateral for hardware for over a decade. |
| Approach: | They propose a benchmark to evaluate LLMs’ effectiveness for assertion generation quantitatively. |
| Outcome: | The proposed benchmark compares state-of-the-art LLMs with existing benchmarks and shows that they generate higher fractions of functionally correct assertions. |
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)
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Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang
| Challenge: | Existing studies on explicit or overt hate speech have failed to address a more pervasive form based on coded or indirect language. |
| Approach: | They propose a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. |
| Outcome: | The proposed dataset will serve as a useful benchmark for understanding this multifaceted issue. |