20 citations · 22 across the 14 of their papers we have counts for
8 papers · 1 filter
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following
Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu +2
Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved…
CoverBench: A Challenging Benchmark for Complex Claim Verification
Alon Jacovi, Moran Ambar, Eyal Ben-David +5
There is a growing line of research on verifying the correctness of language models' outputs. At the same time, LMs are being used to tackle complex queries that require reasoning.…
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
Irina Jurenka, Markus Kunesch, Kevin R. McKee +71
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about…
SEAM: A Stochastic Benchmark for Multi-Document Tasks
Gili Lior, Avi Caciularu, Arie Cattan +3
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a…
Identifying User Goals from UI Trajectories
Omri Berkovitch, Sapir Caduri, Noam Kahlon +3
Identifying underlying user goals and intents has been recognized as valuable in various personalization-oriented settings, such as personalized agents, improved search responses,…
DoubleDipper: Improving Long-Context LLMs via Context Recycling
Arie Cattan, Alon Jacovi, Alex Fabrikant +8
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel…
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools
Avi Caciularu, Alon Jacovi, Eyal Ben-David +5
Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling…
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…