Papers by Arash Einolghozati
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. |
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing (2021.eacl-main)
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| Challenge: | Code-switching (CS) is the alternation of languages within an utterance or conversation. |
| Approach: | They propose to use translation-and-align and augment with a generation model followed by match-and filter to improve CS generalizability of cross-lingual models when data for only one language is available. |
| Outcome: | The proposed models improve when only English data is available alongside zero or a few CS training instances. |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
Sound Natural: Content Rephrasing in Dialog Systems (2020.emnlp-main)
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| Challenge: | Currently, virtual assistants work in the paradigm of intent-slot tagging and the slot values are directly passed as-is to the execution engine. |
| Approach: | They propose to use BART to rephrase a query to make it more natural . they propose to add a copy-pointer and copy loss to it to improve performance . |
| Outcome: | The proposed model improves on existing models by adding a copy-pointer and copy loss. |