92 citations · 308 across the 20 of their papers we have counts for
20 papers
Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning
Yuval Shalev, Amir Feder, Ariel Goldstein
Large language models (LLMs) have shown an impressive ability to perform tasks believed to require thought processes. When the model does not document an explicit thought process,…
Can LLMs Learn Macroeconomic Narratives from Social Media?
Almog Gueta, Amir Feder, Zorik Gekhman +2
This study empirically tests the hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence eco…
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni +4
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured th…
LLMs Accelerate Annotation for Medical Information Extraction
Akshay Goel, Almog Gueta, Omry Gilon +10
The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challenging to access or interpret. To uncove…
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals
Yair Gat, Nitay Calderon, Amir Feder +3
Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions eff…
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…
Evaluating the Moral Beliefs Encoded in LLMs
Nino Scherrer, Claudia Shi, Amir Feder +1
This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components: (1) A stati…
An Invariant Learning Characterization of Controlled Text Generation
Carolina Zheng, Claudia Shi, Keyon Vafa +2
Controlled generation refers to the problem of creating text that contains stylistic or semantic attributes of interest. Many approaches reduce this problem to training a predictor…
Useful Confidence Measures: Beyond the Max Score
Gal Yona, Amir Feder, Itay Laish
An important component in deploying machine learning (ML) in safety-critic applications is having a reliable measure of confidence in the ML model's predictions. For a classifier $…
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Yanai Elazar, Nora Kassner, Shauli Ravfogel +6
Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a…
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder, Guy Horowitz, Yoav Wald +2
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recomm…
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Eldar David Abraham, Karel D'Oosterlinck, Amir Feder +5
The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation…
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation
Nitay Calderon, Eyal Ben-David, Amir Feder +1
Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controlla…
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder, Katherine A. Keith, Emaad Manzoor +10
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same…
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Daniel Rosenberg, Itai Gat, Amir Feder +1
Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal th…
On Calibration and Out-of-domain Generalization
Yoav Wald, Amir Feder, Daniel Greenfeld +1
Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning…
Model Compression for Domain Adaptation through Causal Effect Estimation
Guy Rotman, Amir Feder, Roi Reichart
Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led t…
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Amir Feder, Nadav Oved, Uri Shalit +1
Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning based methods, they are as good as…
Predicting In-game Actions from Interviews of NBA Players
Nadav Oved, Amir Feder, Roi Reichart
Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. While there is an abundance of computati…
Explaining Classifiers with Causal Concept Effect (CaCE)
Yash Goyal, Amir Feder, Uri Shalit +1
How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, w…