49 citations · 130 across the 12 of their papers we have counts for
16 papers
The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
Aileen Cheng, Alon Jacovi, Amir Globerson +62
We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
Arie Cattan, Alon Jacovi, Ori Ram +6
Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources…
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
Ori Yoran, Tomer Wolfson, Ori Ram +1
Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date. An important desideratum of RALM…
Generating Benchmarks for Factuality Evaluation of Language Models
Dor Muhlgay, Ori Ram, Inbal Magar +7
Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing methods f…
In-Context Retrieval-Augmented Language Models
Ori Ram, Yoav Levine, Itay Dalmedigos +4
Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significa…
Parallel Context Windows for Large Language Models
Nir Ratner, Yoav Levine, Yonatan Belinkov +7
When applied to processing long text, Large Language Models (LLMs) are limited by their context window. Existing efforts to address this limitation involve training specialized arc…
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary
Ori Ram, Liat Bezalel, Adi Zicher +3
Dual encoders are now the dominant architecture for dense retrieval. Yet, we have little understanding of how they represent text, and why this leads to good performance. In this w…
Standing on the Shoulders of Giant Frozen Language Models
Yoav Levine, Itay Dalmedigos, Ori Ram +10
Huge pretrained language models (LMs) have demonstrated surprisingly good zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing vision of a single, ve…
Transformer Language Models without Positional Encodings Still Learn Positional Information
Adi Haviv, Ori Ram, Ofir Press +2
Causal transformer language models (LMs), such as GPT-3, typically require some form of positional encoding, such as positional embeddings. However, we show that LMs without any ex…
Learning to Retrieve Passages without Supervision
Ori Ram, Gal Shachaf, Omer Levy +2
Dense retrievers for open-domain question answering (ODQA) have been shown to achieve impressive performance by training on large datasets of question-passage pairs. In this work w…
How Optimal is Greedy Decoding for Extractive Question Answering?
Or Castel, Ori Ram, Avia Efrat +1
Fine-tuned language models use greedy decoding to answer reading comprehension questions with relative success. However, this approach does not ensure that the answer is a span in…
Few-Shot Question Answering by Pretraining Span Selection
Ori Ram, Yuval Kirstain, Jonathan Berant +2
In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the mor…
Coreference Resolution without Span Representations
Yuval Kirstain, Ori Ram, Omer Levy
The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution,…
SenseBERT: Driving Some Sense into BERT
Yoav Levine, Barak Lenz, Or Dagan +6
The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision t…
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
Tal Schuster, Ori Ram, Regina Barzilay +1
We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown t…