565 citations · 1.2k across the 34 of their papers we have counts for
6 papers · 1 filter
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
What Matters for Model Merging at Scale?
Prateek Yadav, Tu Vu, Jonathan Lai +4
Model merging aims to combine multiple expert models into a more capable single model, offering benefits such as reduced storage and serving costs, improved generalization, and sup…
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey +4
Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augme…
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation
Tu Vu, Kalpesh Krishna, Salaheddin Alzubi +3
As large language models (LLMs) advance, it becomes more challenging to reliably evaluate their output due to the high costs of human evaluation. To make progress towards better LL…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in o…