Papers by Vaibhav Adlakha
Understanding the Influence of Synthetic Data for Text Embedders (2025.findings-acl)
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| Challenge: | Recent advances in general purpose text embedders have been driven by training on synthetic training data. |
| Approach: | They propose to use GPT-4 to produce high quality synthetic data that expands existing training datasets for embeddings to new tasks. |
| Outcome: | The proposed dataset is high quality and leads to consistent improvements in performance. |
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching (2022.tacl-1)
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| Challenge: | Current datasets for conversational question answering do not contain topic switches . people often engage in information-seeking conversations to discover new knowledge . |
| Approach: | They propose an open-domain conversational dataset with topic switches based on Wikipedia. |
| Outcome: | The proposed dataset achieves an F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of the dataset. |
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining (2022.findings-emnlp)
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| Challenge: | To explain NLP models, importance measures are often used to inform input tokens are important for making a prediction. |
| Approach: | They propose a faithfulness metric that masks allegedly important tokens and retrains the model. |
| Outcome: | The proposed metric is based on LSTM-attention models and RoBERTa models. |
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction (2020.emnlp-main)
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| Challenge: | OpenIE generates extractions iteratively, requiring repeated encoding of partial outputs. |
| Approach: | They propose an iterative open information extraction system that generates extractions iterativly, requiring repeated encoding of partial outputs. |
| Outcome: | The proposed system beats the previous systems by as much as 4 pts in F1 while being much faster. |
Image Retrieval from Contextual Descriptions (2022.acl-long)
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| Challenge: | a new multimodal challenge challenges vision-and-language models to integrate context into their representations. |
| Approach: | They propose a multimodal challenge to integrate context into vision-and-language models . they benchmark several state-of-the-art models using cross-encoders and bi-encodings . |
| Outcome: | The proposed model lags behind human models on imageCoDe, compared with human models. |