Papers by Alon Jacovi
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks (2023.emnlp-main)
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| Challenge: | Common NLP models are trained on data crawled from the internet, and it is difficult to audit at scale. |
| Approach: | They propose three strategies to prevent data contamination by encrypting test data and preventing it from being released on the internet. |
| Outcome: | The proposed strategies can make a difference in preventing data contamination. |
Contrastive Explanations for Model Interpretability (2021.emnlp-main)
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| Challenge: | Existing methods for producing model explanations seek all causal factors at once, making them difficult to comprehend. |
| Approach: | They propose a method to produce contrastive explanations in the latent space . they use attribution and token/span attribution to produce models that consider only contrastive reasoning . |
| Outcome: | The proposed method allows model behavior to consider only contrastive reasoning . it also uncovers which aspects of the input are useful for and against particular decisions . |
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning (2021.eacl-main)
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| Challenge: | Current methods for document set expansion for large collections are based on word-frequency or bag-of-words document similarity metrics. |
| Approach: | They propose to extend the IR approach by treating the problem as an instance of positive-unlabeled (PU) learning . they propose solutions for each challenge and empirically validate them with ablation tests . |
| Outcome: | The proposed method improves on a PubMed abstract retrieval task . it is compared with existing methods and empirically validated with ablation tests . |
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data (2020.emnlp-main)
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| Challenge: | Existing methods for relation extraction (RE) use shallow heuristics that do not generalize to challenge-set data. |
| Approach: | They propose to annotate a dataset to test whether relation extraction models are generalized to the challenge-set data. |
| Outcome: | The proposed model performs better on the challenge-set compared with the SOTA models on the same dataset. |
Neighboring Words Affect Human Interpretation of Saliency Explanations (2023.findings-acl)
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| Challenge: | Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. |
| Approach: | They conduct a user study to examine how the marking of a word’s *neighboring words* affect the explainee’s perception of the word’ s importance in the context of . a saliency explanation. |
| Outcome: | The findings question whether text-based saliency explanations should continue to be communicated at word level and inform future research on alternative methods. |
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? (2020.acl-main)
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| Challenge: | Current approaches to interpretability evaluation focus on faithfulness criteria . current approaches focus on readability, plausibility and faithfulness . |
| Approach: | They argue that current binary definition of faithfulness sets unrealistic standards . they argue that a more graded definition would be of greater practical utility . |
| Outcome: | The proposed approach is based on three assumptions and lacks a graded definition of faithfulness. |
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals (2021.tacl-1)
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| Challenge: | Amnesic probing is a method that focuses on how information is being used, rather than on what information is encoded. |
| Approach: | They propose a method that focuses on how the information is being used rather than on what information is encoded. |
| Outcome: | The proposed method is based on a BERT dataset to ask questions that were not possible before . it shows that probing performance is not correlated to task importance . |
A Comprehensive Evaluation of Tool-Assisted Generation Strategies (2023.findings-emnlp)
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| Challenge: | Various few-shot tool-usage strategies have been proposed to overcome LMs' shortcomings. |
| Approach: | They propose to augment language models with tools to overcome their shortcomings . they find strong no-tool baselines are competitive to tool-assisted strategies . |
| Outcome: | The proposed strategies outperform those that refine incorrect outputs with tools in knowledge-retrieval tasks, the study finds . the findings suggest few-shot tool integration is still an open challenge . |
Aligning Faithful Interpretations with their Social Attribution (2021.tacl-1)
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| Challenge: | a common pattern of faithfulness is decomposition of a model into steps and inspecting the intermediate steps. |
| Approach: | They propose to formalize faithfulness as an accurate attribution of causality to the model . they propose an alternative causal chain to remedy the issues . |
| Outcome: | The proposed model interpretations fail because they are inconsistent and vague . the proposed model is based on a social attribution model and a highlight method . |
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains (2024.acl-long)
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Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, Mor Geva
| Challenge: | Recent literature discusses automatic methods to evaluate reasoning to improve their correctness, but no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods. |
| Approach: | They propose to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question-answering settings using a dataset that includes comprehensive labels for relevance, attribution to evidence passages, and logical correctness of each reasoning step. |
| Outcome: | The proposed dataset shows that verifiers struggle at verifying reasoning chains, particularly verifying logical correctness and detecting contradictions. |
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP (2024.emnlp-main)
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| Challenge: | Improvements in language models’ capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. |
| Approach: | They propose to unpack the taxonomy of long-context based on the properties that make them more difficult with longer contexts. |
| Outcome: | The proposed taxonomy is based on the properties that make them more difficult with longer contexts. |
ConSim: Measuring Concept-Based Explanations’ Effectiveness with Automated Simulatability (2025.acl-long)
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| Challenge: | Existing evaluation metrics focus only on the quality of the induced space of possible concepts, neglecting the latter. |
| Approach: | They propose to use large language models as simulators to approximate the evaluation and report various analyses to make such approximations reliable. |
| Outcome: | The proposed framework allows for scalable and consistent evaluation across models and datasets. |