activity
20222026
most citedLaMDA: Language Models for Dialog Applications

708 citations · 1.2k across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2026★ 3 cited

Towards Autonomous Mathematics Research

Tony Feng, Trieu H. Trinh, Garrett Bingham +25

Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from com…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.CL2024

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…

cs.CL2024

In Tree Structure Should Sentence Be Generated

Yaguang Li, Xin Chen

Generative models reliant on sequential autoregression have been at the forefront of language generation for an extensive period, particularly following the introduction of widely…

cs.CL2024★ 297 cited

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…

cs.CL2024

Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks

R. Patrick Xian, Alex J. Lee, Satvik Lolla +4

The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…

cs.LG2023★ 2 cited

Controlled Decoding from Language Models

Sidharth Mudgal, Jong Lee, Harish Ganapathy +10

KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective a…

cs.CL2023★ 168 cited

PaLM 2 Technical Report

Rohan Anil, Andrew M. Dai, Orhan Firat +125

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…

cs.CL2022★ 29 cited

HyperPrompt: Prompt-based Task-Conditioning of Transformers

Yun He, Huaixiu Steven Zheng, Yi Tay +9

Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way. Here, we explore the use of HyperNetworks to generate hyper-prompts: we pro…

cs.CL2022★ 708 cited

LaMDA: Language Models for Dialog Applications

Romal Thoppilan, Daniel De Freitas, Jamie Hall +57

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…