5 citations · 20 across the 17 of their papers we have counts for
17 papers
Autodata: An agentic data scientist to create high quality synthetic data
Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu +12
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) s…
Self-Improving Pretraining: using post-trained models to pretrain better models
Ellen Xiaoqing Tan, Jack Lanchantin, Shehzaad Dhuliawala +9
Large language models are classically trained in stages: pretraining on raw text followed by post-training for instruction following and reasoning. However, this separation creates…
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
The Era of Real-World Human Interaction: RL from User Conversations
Chuanyang Jin, Jing Xu, Bo Liu +6
We posit that to achieve continual model improvement and multifaceted alignment, future models must learn from natural human interaction. Current conversational models are aligned…
StepWiser: Stepwise Generative Judges for Wiser Reasoning
Wei Xiong, Wenting Zhao, Weizhe Yuan +4
As models increasingly leverage multi-step reasoning strategies to solve complex problems, supervising the logical validity of these intermediate steps has become a critical resear…
CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
Ping Yu, Jack Lanchantin, Tianlu Wang +6
We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on given seed tasks, and then generat…
Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models
Thao Nguyen, Yang Li, Olga Golovneva +4
Scaling laws predict that the performance of large language models improves with increasing model size and data size. In practice, pre-training has been relying on massive web craw…
Multi-Token Attention
Olga Golovneva, Tianlu Wang, Jason Weston +1
Soft attention is a critical mechanism powering LLMs to locate relevant parts within a given context. However, individual attention weights are determined by the similarity of only…
R.I.P.: Better Models by Survival of the Fittest Prompts
Ping Yu, Weizhe Yuan, Olga Golovneva +4
Training data quality is one of the most important drivers of final model quality. In this work, we introduce a method for evaluating data integrity based on the assumption that lo…
Self-Taught Evaluators
Tianlu Wang, Ilia Kulikov, Olga Golovneva +7
Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the s…
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
Tianhao Wu, Weizhe Yuan, Olga Golovneva +5
Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding m…
Contextual Position Encoding: Learning to Count What's Important
Olga Golovneva, Tianlu Wang, Jason Weston +1
The attention mechanism is a critical component of Large Language Models (LLMs) that allows tokens in a sequence to interact with each other, but is order-invariant. Incorporating…
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Sainbayar Sukhbaatar, Olga Golovneva, Vasu Sharma +8
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowled…
Reverse Training to Nurse the Reversal Curse
Olga Golovneva, Zeyuan Allen-Zhu, Jason Weston +1
Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse. Ev…
Efficient Tool Use with Chain-of-Abstraction Reasoning
Silin Gao, Jane Dwivedi-Yu, Ping Yu +7
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and ph…
PathFinder: Guided Search over Multi-Step Reasoning Paths
Olga Golovneva, Sean O'Brien, Ramakanth Pasunuru +4
With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However…
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
Peifang Wang, Olga Golovneva, Armen Aghajanyan +4
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at a…