Papers by Sahithya Ravi
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models (2024.emnlp-main)
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| Challenge: | Vision-Language Models (VLMs) have shown emerging capabilities through large-scale training that have made them gain popularity in recent years. |
| Approach: | They propose to perform retrieval across universals and cultural visual grounding tasks to assess cultural diversity across universal and culture-specific local concepts. |
| Outcome: | The proposed benchmarks show that the models perform significantly across cultures, underscoring the need for enhancing multicultural understanding in vision-language models. |
Small But Funny: A Feedback-Driven Approach to Humor Distillation (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have been used to transfer knowledge from LLMs to smaller, smaller language models (SLMs). |
| Approach: | They propose to assign a dual role to the LLM as a “teacher” generating data, as well as evaluating the student’s performance. |
| Outcome: | The proposed approach narrows the performance gap between LLMs and larger models by incorporating feedback into the data. |
CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs’ Cultural Knowledge Through Human-AI Red-Teaming (2025.acl-long)
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Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, Yejin Choi
| Challenge: | CulturalBench is a set of 1,696 human-written and human-verified questions to assess LMs’ cultural knowledge covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru. |
| Approach: | They construct a set of 1,696 human-written and human-verified questions to assess LMs' cultural knowledge, covering 45 global regions including underrepresented ones like Bangladesh, Zimbabwe, and Peru. |
| Outcome: | The proposed model outperforms other models across cultures, while underperforming on questions related to North Africa, South America and Middle East. |
CASE: Commonsense-Augmented Score with an Expanded Answer Space (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive zero-shot performance on NLP tasks thanks to the knowledge they acquired in their training. |
| Approach: | They propose a Commonsense-Augmented Score with an Expanded Answer Space that assigns importance weights to words based on their semantic relations to other words in the input. |
| Outcome: | The proposed approach outperforms basic LM scores on 5 commonsense benchmarks and is complementary to previous approaches. |
COMET-M: Reasoning about Multiple Events in Complex Sentences (2023.findings-emnlp)
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| Challenge: | Existing commonsense models that generate event-centric inferences for simple sentences struggle with the complexity of multi-event sentences prevalent in natural text. |
| Approach: | They propose a commonsense model that generates inferences for a target event within a complex sentence using a multi-event inference dataset. |
| Outcome: | The proposed model produces inferences for a target event within a complex sentence taking the complete context into account. |
What happens before and after: Multi-Event Commonsense in Event Coreference Resolution (2023.eacl-main)
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| Challenge: | Existing event coreference models cluster event mentions pertaining to the same event, but they fail to leverage commonsense inferences for lexically-divergent mentions. |
| Approach: | They propose a model that extends event mentions with temporal commonsense inferences to generate plausible events that happen before and after the target events. |
| Outcome: | The proposed model generates plausible events that happen before and after the target event, and then after it, such as "he was sentenced". |
Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames (2025.emnlp-main)
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Sahithya Ravi, Gabriel Herbert Sarch, Vibhav Vineet, Andrew D Wilson, Balasaravanan Thoravi Kumaravel
| Challenge: | Disjoint-3DQA evaluates the spatial reasoning ability of embodied AI assistants based on egocentric video . it aims to catalyze future research at the intersection of vision, language, and embodie . |
| Approach: | They propose a generative QA benchmark that evaluates the ability of embodied AI assistants to integrate spatial cues across time by asking object pairs that are not co-visible in the same frame. |
| Outcome: | The proposed benchmark compares seven state-of-the-art VLMs and finds that they lag behind human performance by 28%, with steeper declines as the temporal gap widens. |
Grounding Task Assistance with Multimodal Cues from a Single Demonstration (2025.findings-acl)
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Gabriel Herbert Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi, Vibhav Vineet, Andrew D Wilson
| Challenge: | RGB video often fails to capture fine-grained contextual cues such as intent, safety-critical environmental factors, and subtle preferences embedded in human behavior. |
| Approach: | They propose a framework that integrates eye gaze and speech cues to improve conversational agents for task assistance by integrating eye gaze with speech cuests. |
| Outcome: | The proposed framework captures fine-grained intent and user-specific cues, enabling richer contextual grounding for visual question answering. |