Papers by Oren Barkan
Bayesian Hierarchical Words Representation Learning (2020.acl-main)
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| Challenge: | Using hierarchical priors, BHWR improves word representations by incorporating word semantic taxonomy. |
| Approach: | They propose a Bayesian Hierarchical Words Representation (BHWR) learning algorithm that integrates hierarchical priors and word semantic taxonomy to improve representations. |
| Outcome: | The proposed model performs better on rare words and on linguistic datasets than other methods. |
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
| Outcome: | The proposed model outperforms existing methods on large documents datasets. |
Within-Between Lexical Relation Classification (2020.emnlp-main)
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| Challenge: | Existing methods for recognizing lexical-semantic relations between words are path-based and distributional. |
| Approach: | They propose a novel Within-Between Relation model for recognizing lexical-semantic relations between words. |
| Outcome: | The proposed model outperforms baselines across various benchmarks and is competitive and competitive. |
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. |
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers (2024.acl-long)
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| Challenge: | Existing methods for detecting hallucinations in large language models are limited due to their high frequency and high accuracy. |
| Approach: | They propose a method to detect hallucinations in large language models by repeating model-generated responses from its generated answer. |
| Outcome: | The proposed method achieves 87% hallucinations in a specific experiment without external knowledge. |
Improving LLM Attributions with Randomized Path-Integration (2024.findings-emnlp)
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| Challenge: | Recent advances in AI research have impacted numerous application domains, fueling innovation and progress in user modeling and personalization. |
| Approach: | They propose a path-integration method for explaining language models via randomization of the integration path over the attention information in the model. |
| Outcome: | The proposed method outperforms state-of-the-art methods across 4 datasets and 5 evaluation metrics. |
LLM Explainability via Attributive Masking Learning (2024.findings-emnlp)
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| Challenge: | In this paper, we introduce Attributive Masking Learning (AML), a method designed for explaining language model predictions by learning input masks. |
| Approach: | They introduce a method for explaining language model predictions by learning input masks and ensuring a significant change in the model's explanation when applying the inverse mask to the input. |
| Outcome: | The proposed method outperforms state-of-the-art explanation methods on multiple benchmarks. |