Papers by Ori Katz
Answering Questions by Meta-Reasoning over Multiple Chains of Thought (2023.emnlp-main)
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| Challenge: | Modern systems for multi-hop question answering (QA) break questions into a sequence of reasoning steps, termed chain-of-thought (CoT) Often, multiple chains are sampled and aggregated, but the intermediate steps themselves are discarded. |
| Approach: | They propose a method which prompts large language models to meta-reason over multiple chains of thought rather than aggregate their answers. |
| Outcome: | The proposed approach outperforms baselines on 7 multi-hop QA datasets. |
RecoBERT: A Catalog Language Model for Text-Based Recommendations (2020.findings-emnlp)
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| Challenge: | RecoBERT is a BERT-based approach for learning catalog-specialized language models for text-based item recommendations. |
| Approach: | They propose a BERT-based approach for learning catalog-specialized language models for text-based item recommendations that incorporates four scores during inference. |
| Outcome: | The proposed model can infer item-to-item similarities more accurately than other methods. |