Papers by Seyed Hossein Alavi
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? (2020.lrec-1)
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| Challenge: | End-to-end neural network models of conversational dialogue are popular for conversational tasks, but there are still questions about how well they work for real applications and how much data is needed to achieve acceptable performance. |
| Approach: | They compare two different kinds of end-to-end dialogue models based on cross-language relevance and cross-linguistic LSTM models for corpus-based selection of dialogue responses. |
| Outcome: | The proposed models perform well on a large corpus, but are dominated by a more moderate-sized corpus. |
Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models (2024.eacl-long)
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Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, Vered Shwartz
| Challenge: | Recent work suggests that Large Language Models (LLMs) exhibit Neural Theory-of-Mind (N-ToM) however, prior work reached conflicting conclusions regarding those abilities. |
| Approach: | They examine the extent of Large Language Models’ N-ToM abilities through an extensive evaluation of 6 tasks and find that LLMs struggle with adversarial examples . |
| Outcome: | The proposed metrics show that LLMs exhibit certain N-ToM abilities, but this behavior is far from robust. |
Interactive Evaluation of Dialog Track at DSTC9 (2022.lrec-1)
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| Challenge: | Currently, dialog research is focused on static data, which neglects multiple important properties of dialog, such as consistency, topic depth, adaptation, error recovery and user-centric development. |
| Approach: | They propose to use static dialogs to build strong response generation models and extend them to back-and-forth interactions with real users. |
| Outcome: | The proposed model trains a larger evolved Transformer model on social media data and attains strong performance in interactive settings. |