464 citations · 478 across the 8 of their papers we have counts for
10 papers
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Howard Yen, Tianyu Gao, Minmin Hou +5
Many benchmarks exist for evaluating long-context language models (LCLMs), yet developers often rely on synthetic tasks such as needle-in-a-haystack (NIAH) or an arbitrary subset o…
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat +1
Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, eval…
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
Moshe Berchansky, Daniel Fleischer, Moshe Wasserblat +1
State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their resp…
Optimizing Retrieval-augmented Reader Models via Token Elimination
Moshe Berchansky, Peter Izsak, Avi Caciularu +2
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc. In FiD, su…
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…
How to Train BERT with an Academic Budget
Peter Izsak, Moshe Berchansky, Omer Levy
While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train…
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
Peter Izsak, Shira Guskin, Moshe Wasserblat
Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuou…
Q8BERT: Quantized 8Bit BERT
Ofir Zafrir, Guy Boudoukh, Peter Izsak +1
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models co…
Term Set Expansion based NLP Architect by Intel AI Lab
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +5
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an i…
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +7
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an…