Papers by Uri Katz
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models (2021.emnlp-main)
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| Challenge: | Existing paradigms for multi-task training involve a shared pre-trained language model and a small, thin network (head) given an input, a target head is the head that is selected for outputting the final prediction. |
| Approach: | They examine the behaviour of non-target heads when given input that belongs to a different task than the one they were trained for. |
| Outcome: | The non-target heads exhibit emergent behaviour, which may explain the target task, or generalize beyond their original task. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)
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| Challenge: | NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type. |
| Approach: | They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type . |
| Outcome: | The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks. |
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. |
Inferring Implicit Relations in Complex Questions with Language Models (2022.findings-emnlp)
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| Challenge: | A prominent challenge for language understanding systems is the ability to answer implicit reasoning questions where the evidence for answering the question is not mentioned explicitly. |
| Approach: | They propose to decouple inference of reasoning steps from execution by evaluating models of implicit relation inference. |
| Outcome: | The proposed model fails on the implicit reasoning QA task, but infers implicit relations . the proposed model is compared with other models that fail on the same task . |
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature (2024.findings-emnlp)
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| Challenge: | Knowledge Navigator organizes retrieved documents into a navigable, two-level hierarchy of named and descriptive topics and subtopics. |
| Approach: | They propose to organize retrieved scientific documents into a navigable, two-level hierarchy of named and descriptive topics and subtopics. |
| Outcome: | The proposed system provides an overall view of the research themes in a domain while also enabling iterative search and deeper knowledge discovery within specific subtopics. |