Papers by Vijjini Anvesh Rao
Towards Inter-character Relationship-driven Story Generation (2022.emnlp-main)
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| Challenge: | Recent story generation methods can generate stories based on open-ended prompts and planners but can neither encode character relationships nor give explicit control over the characters and their relationships. |
| Approach: | They propose a model that uses relationships as latent variables for story generation and propose 'relationship-driven' story generation. |
| Outcome: | The proposed model generates stories sentence by sentence with relationships that are more faithful to desired relationships while maintaining the content quality. |
Curricular Next Conversation Prediction Pretraining for Transcript Segmentation (2023.findings-eacl)
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| Challenge: | Prior research on document segmentation has focused on segmenting documents such as Wikipedia articles. |
| Approach: | They propose to pretrain a model to identify consecutive conversations to address these challenges . they introduce a curriculum to Advanced NCP to make the task more relevant to the downstream task . |
| Outcome: | The proposed model outperforms previous models in speech recognition errors and is robust to speech recognition. |
Sequential Learning of Convolutional Features for Effective Text Classification (D19-1)
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| Challenge: | Existing models for text classification have largely ignored convolution filters and max pooling . text classification is one of the major applications of natural language processing . |
| Approach: | They propose a convolutional attentive recurrent network model which uses convolution filters and max pooling to improve text classification. |
| Outcome: | The proposed model outperforms existing convolutional models on text classification tasks. |
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models (2024.findings-emnlp)
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| Challenge: | Increasingly, large language models (LLMs) are able to understand and rationalize socially acceptable behaviors, but they are often misaligned with human consensus. |
| Approach: | They propose a multi-step prompting framework that verbalizes a social situation from multiple perspectives before forming a judgment. |
| Outcome: | The proposed framework improves the alignment with human judgments by up to 11 F1 points with the GPT-3.5 model. |
Exploring Safety-Utility Trade-Offs in Personalized Language Models (2025.naacl-long)
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| Challenge: | Prior studies have shown that large language models can exhibit bias against specific demographic groups and engage in the generation of stereotypical responses. |
| Approach: | They propose a framework to evaluate LLM performance along two axes: safety and utility. |
| Outcome: | The proposed framework evaluates the performance of LLMs along two axes: safety and utility. |
Twitter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction (C18-1)
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| Challenge: | a majority of research studies on twitter focus on English tweets, despite the fact that English dominates the mix of languages. |
| Approach: | They leverage social media platforms such as twitter for developing corpus across multiple languages . they use tweets to collect data for sentiment analysis and emoji prediction . |
| Outcome: | The proposed method is applicable for resource-scarce languages provided speakers of that particular language are active users on social media platforms. |
Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations? (2026.acl-long)
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| Challenge: | Power differences shape human communication through well-documented socio-cognitive effects . asymmetric relationships or power differentials give rise to well-known socio-computational effects - lianelli, 1976 . |
| Approach: | They simulate multi-turn, power-asymmetric dialogues with personas from diverse professions . they find that LLMs show key socio-cognitive effects of power, albeit with nuances and variability . |
| Outcome: | The results show that large language models exhibit socio-cognitive effects of power . the results are consistent with previous studies on LLMs . |
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations (P18-3)
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| Challenge: | Existing sentiment analysis systems have a lot of scope for improvement to meet the standards of the end users. |
| Approach: | They propose to generate a systematically annotated corpus that can support sentiment analysis tasks in Telugu using word-level sentiment annotations. |
| Outcome: | The proposed resource can be used to improve sentiment analysis tasks in Telugu using word-level sentiment annotations. |