66 citations · 203 across the 23 of their papers we have counts for
20 papers · 1 filter
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models
Bang An, Shiyue Zhang, Mark Dredze
Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…
The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
Alon Jacovi, Andrew Wang, Chris Alberti +23
We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to giv…
ConSim: Measuring Concept-Based Explanations' Effectiveness with Automated Simulatability
Antonin Poché, Alon Jacovi, Agustin Martin Picard +2
Concept-based explanations work by mapping complex model computations to human-understandable concepts. Evaluating such explanations is very difficult, as it includes not only the…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
CoverBench: A Challenging Benchmark for Complex Claim Verification
Alon Jacovi, Moran Ambar, Eyal Ben-David +5
There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning.…
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Omer Goldman, Alon Jacovi, Aviv Slobodkin +3
Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. Howeve…
DoubleDipper: Improving Long-Context LLMs via Context Recycling
Arie Cattan, Alon Jacovi, Alex Fabrikant +8
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel…
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools
Avi Caciularu, Alon Jacovi, Eyal Ben-David +5
Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling…
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi, Yonatan Bitton, Bernd Bohnet +6
Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typ…
A Comprehensive Evaluation of Tool-Assisted Generation Strategies
Alon Jacovi, Avi Caciularu, Jonathan Herzig +3
A growing area of research investigates augmenting language models with tools (e.g., search engines, calculators) to overcome their shortcomings (e.g., missing or incorrect knowled…
Neighboring Words Affect Human Interpretation of Saliency Explanations
Alon Jacovi, Hendrik Schuff, Heike Adel +2
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such…
Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
Alon Jacovi, Avi Caciularu, Omer Goldman +1
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a tr…
Human Interpretation of Saliency-based Explanation Over Text
Hendrik Schuff, Alon Jacovi, Heike Adel +2
While a lot of research in explainable AI focuses on producing effective explanations, less work is devoted to the question of how people understand and interpret the explanation.…
Contrastive Explanations for Model Interpretability
Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel +3
Contrastive explanations clarify why an event occurred in contrast to another. They are more inherently intuitive to humans to both produce and comprehend. We propose a methodology…
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
Shachar Rosenman, Alon Jacovi, Yoav Goldberg
The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We ident…
Aligning Faithful Interpretations with their Social Attribution
Alon Jacovi, Yoav Goldberg
We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case-study, we present several failure…
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi +1
A growing body of work makes use of probing to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitatio…
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
Alon Jacovi, Yoav Goldberg
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretat…
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
Alon Jacovi, Gang Niu, Yoav Goldberg +1
We consider the situation in which a user has collected a small set of documents on a cohesive topic, and they want to retrieve additional documents on this topic from a large coll…
Understanding Convolutional Neural Networks for Text Classification
Alon Jacovi, Oren Sar Shalom, Yoav Goldberg
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters i…