8 citations · 16 across the 28 of their papers we have counts for
20 papers · 1 filter
Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes
Jeremias Ferrao, Niclas Müller-Hof, Iustin Sîrbu +2
We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS, a human-annotated dataset…
Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles
Shun Shao, Zheng Zhao, Anna Korhonen +2
Most fairness research in NLP assumes direct access to protected attributes such as gender, race, or nationality. In practice, however, such information is often unavailable due to…
When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation
Mingyan Wu, Han Yang, Omer Ben-Porat +1
Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricted, or otherwise costly to ac…
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
Anum Afzal, Florian Matthes, Gal Chechik +1
We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representat…
Iterative Multilingual Spectral Attribute Erasure
Shun Shao, Yftah Ziser, Zheng Zhao +3
Multilingual representations embed words with similar meanings to share a common semantic space across languages, creating opportunities to transfer debiasing effects between langu…
Aligning What LLMs Do and Say: Towards Self-Consistent Explanations
Sahar Admoni, Ofra Amir, Assaf Hallak +1
Large language models (LLMs) seem to offer an easy path to interpretability: just ask them to explain their answers. Yet the features driving an answer often differ from those emph…
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
Zheng Zhao, Yftah Ziser, Shay B. Cohen
Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models that can solve various natural language processing…
Spectral Editing of Activations for Large Language Model Alignment
Yifu Qiu, Zheng Zhao, Yftah Ziser +3
Large language models (LLMs) often exhibit undesirable behaviours, such as generating untruthful or biased content. Editing their internal representations has been shown to be effe…
Are Large Language Models Temporally Grounded?
Yifu Qiu, Zheng Zhao, Yftah Ziser +3
Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we p…
A Joint Matrix Factorization Analysis of Multilingual Representations
Zheng Zhao, Yftah Ziser, Bonnie Webber +1
We present an analysis tool based on joint matrix factorization for comparing latent representations of multilingual and monolingual models. An alternative to probing, this tool al…
Detecting and Mitigating Hallucinations in Multilingual Summarisation
Yifu Qiu, Yftah Ziser, Anna Korhonen +2
Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lac…
BERT is not The Count: Learning to Match Mathematical Statements with Proofs
Weixian Waylon Li, Yftah Ziser, Maximin Coavoux +1
We introduce a task consisting in matching a proof to a given mathematical statement. The task fits well within current research on Mathematical Information Retrieval and, more gen…
Erasure of Unaligned Attributes from Neural Representations
Shun Shao, Yftah Ziser, Shay Cohen
We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is impli…
Understanding Domain Learning in Language Models Through Subpopulation Analysis
Zheng Zhao, Yftah Ziser, Shay B. Cohen
We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of…
Domain Adaptation from Scratch
Eyal Ben-David, Yftah Ziser, Roi Reichart
Natural language processing (NLP) algorithms are rapidly improving but often struggle when applied to out-of-distribution examples. A prominent approach to mitigate the domain gap…
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
Marcio Fonseca, Yftah Ziser, Shay B. Cohen
We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers. Our method, Facto…
Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information
Shun Shao, Yftah Ziser, Shay B. Cohen
We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove private or guarded information from neural representations. Our method uses matrix decompositi…
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction
Entony Lekhtman, Yftah Ziser, Roi Reichart
The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pr…
Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products
Ohad Rozen, David Carmel, Avihai Mejer +2
Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is…
Neural Structural Correspondence Learning for Domain Adaptation
Yftah Ziser, Roi Reichart
Domain adaptation, adapting models from domains rich in labeled training data to domains poor in such data, is a fundamental NLP challenge. We introduce a neural network model that…