Papers by Amir Feder
An Invariant Learning Characterization of Controlled Text Generation (2023.acl-long)
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| Challenge: | Controlled generation is a problem of creating text that contains stylistic or semantic attributes of interest. |
| Approach: | They propose a distribution shift-based control system that can be used to train a predictor of the desired attribute. |
| Outcome: | The proposed method shows that the most effective predictor should be invariant across multiple text environments. |
Exploring the Learning Capabilities of Language Models using LEVERWORLDS (2024.emnlp-main)
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| Challenge: | Existing models of stochastic learning involve learning general structure rules and specific properties of the instance. |
| Approach: | They propose a framework that allows the generation of physics-inspired worlds that follow a similar generative process with different distributions and their instances can be expressed in natural language. |
| Outcome: | The proposed framework allows the generation of physics-inspired worlds that follow a similar generative process with different distributions and their instances can be expressed in natural language. |
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
| Challenge: | causality has not had the same importance in natural language processing, says aaron e. smith . he says research on causality in NLP remains scattered across domains without unified definitions . |
| Approach: | They propose to consolidate research on causality in NLP across academic areas . they explore potential uses of causal inference to improve robustness, fairness, interpretability . |
| Outcome: | The proposed method is a unified overview of causal inference for the NLP community. |
Can LLMs Learn Macroeconomic Narratives from Social Media? (2025.findings-naacl)
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| Challenge: | Existing evaluation strategies for analyzing economic data with narratives are limited due to the complexity of the interplay of numerous factors and the difficulty in isolating causal relationships. |
| Approach: | They propose to use two Twitter datasets to capture economy-related narratives and use them to construct models using large language models. |
| Outcome: | The proposed models are able to predict macroeconomic fluctuations using the extracted or extracted narratives in two Twitter datasets. |
Confidence Improves Self-Consistency in LLMs (2025.findings-acl)
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| Challenge: | Modern large language models (LLMs) demonstrate strong reasoning capabilities, driven in part by their capacity to generate a sequence of intermediate reasoning steps that lead them toward a final answer. |
| Approach: | They propose a method that performs a weighted majority vote based on confidence scores obtained directly from the model. |
| Outcome: | The proposed method outperforms self-consistency on nine models and four datasets, reducing the required number of reasoning paths by over 40% on average. |
Learning and Evaluating a Differentially Private Pre-trained Language Model (2021.findings-emnlp)
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Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias
| Challenge: | Contextual language models have improved performance but can lead to information leakage . |
| Approach: | They propose a differentially-private word-piece algorithm that allows training a tailored domain-specific vocabulary while maintaining privacy. |
| Outcome: | The proposed model can guarantee privacy while maintaining good model performance. |
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)
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| Challenge: | Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process. |
| Approach: | They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text. |
| Outcome: | The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm. |
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? (2024.emnlp-main)
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| Challenge: | Pre-training Large Language Models (LLMs) on textual corpora embeds substantial factual knowledge in their parameters, which is essential for excelling in various downstream applications. |
| Approach: | They propose to use supervised fine-tuning to align large language models to new factual information that is not acquired through pre-training. |
| Outcome: | The proposed model is trained to generate facts that are not grounded in pre-existing knowledge, but hallucinates when examples with new knowledge are learned. |
Model Compression for Domain Adaptation through Causal Effect Estimation (2021.tacl-1)
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| Challenge: | Existing methods for compressing language representation models are not interpretable and do not consider the differences in the predictive power of various model components or the generalizability of the compressed models. |
| Approach: | They propose a model compression scheme that estimates the average treatment effect of a single layer on the model's predictions. |
| Outcome: | The proposed model compression scheme outperforms strong baselines on dozens of domain pairs across three text classification and sequence tagging tasks. |
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
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| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions (2021.acl-short)
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| Challenge: | Existing systems for visual question answering are overfitted to training data and are sensitive to small perturbations. |
| Approach: | They propose a robustness measure to augment visual question answering datasets to measure generalization capabilities. |
| Outcome: | The proposed model can quantify failure cases which reveal that current systems are still brittle. |