67 citations · 156 across the 16 of their papers we have counts for
17 papers
Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation
Gili Lior, Tzviel Frostig, Gabriel Stanovsky +1
Multilingual benchmarks are central to evaluating large language models (LLMs) across languages, but they suffer from three issues: exhaustive evaluation scales linearly with the n…
Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs
Guy Mor-Lan, Omer Goldman, Matan Eyal +6
Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of biased behavior, as knowledge a…
FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images
Sabab Ishraq, Aarushi Aarushi, Juncai Jiang +1
Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision language research on food has fo…
Assay2Mol: large language model-based drug design using BioAssay context
Yifan Deng, Spencer S. Ericksen, Anthony Gitter
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer
Omer Goldman, Uri Shaham, Dan Malkin +11
To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current…
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…
Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?
Yotam Intrator, Matan Halfon, Roman Goldenberg +5
Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the wides…
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance
Omer Goldman, Avi Caciularu, Matan Eyal +3
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear. In this paper, we argue…
The Hidden Space of Transformer Language Adapters
Jesujoba O. Alabi, Marius Mosbach, Matan Eyal +2
We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages. We sh…
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
Uri Shaham, Jonathan Herzig, Roee Aharoni +3
As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. In this work, we in…
Multilingual Sequence-to-Sequence Models for Hebrew NLP
Matan Eyal, Hila Noga, Roee Aharoni +2
Recent work attributes progress in NLP to large language models (LMs) with increased model size and large quantities of pretraining data. Despite this, current state-of-the-art LMs…
Large Scale Substitution-based Word Sense Induction
Matan Eyal, Shoval Sadde, Hillel Taub-Tabib +1
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus…
Bootstrapping Relation Extractors using Syntactic Search by Examples
Matan Eyal, Asaf Amrami, Hillel Taub-Tabib +1
The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a…
Interactive Extractive Search over Biomedical Corpora
Hillel Taub-Tabib, Micah Shlain, Shoval Sadde +4
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using pat…
Question Answering as an Automatic Evaluation Metric for News Article Summarization
Matan Eyal, Tal Baumel, Michael Elhadad
Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, eval…
Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
Tal Baumel, Matan Eyal, Michael Elhadad
Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstr…