380 citations · 487 across the 16 of their papers we have counts for
18 papers
Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization
Or Raphael Bidusa, Shaul Markovitch
The opaque nature of Large Language Models (LLMs) has led to significant research efforts aimed at enhancing their interpretability, primarily through post-hoc methods. More recent…
A General Search-based Framework for Generating Textual Counterfactual Explanations
Daniel Gilo, Shaul Markovitch
One of the prominent methods for explaining the decision of a machine-learning classifier is by a counterfactual example. Most current algorithms for generating such examples in th…
Interpreting Embedding Spaces by Conceptualization
Adi Simhi, Shaul Markovitch
One of the main methods for computational interpretation of a text is mapping it into a vector in some embedding space. Such vectors can then be used for a variety of textual proce…
Knowledge-Based Learning through Feature Generation
Michal Badian, Shaul Markovitch
Machine learning algorithms have difficulties to generalize over a small set of examples. Humans can perform such a task by exploiting vast amount of background knowledge they poss…
A Two-Stage Masked LM Method for Term Set Expansion
Guy Kushilevitz, Shaul Markovitch, Yoav Goldberg
We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical ut…
Textual Membership Queries
Jonathan Zarecki, Shaul Markovitch
Human labeling of data can be very time-consuming and expensive, yet, in many cases it is critical for the success of the learning process. In order to minimize human labeling effo…