activity
20192026
most citedThe Amazon Nova Family of Models: Technical Report and Model Card

2 citations · 5 across the 29 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CL2024

Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM

Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2

Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various L…

cs.CL2024

Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification

Tao Meng, Ninareh Mehrabi, Palash Goyal +6

We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. Given a training corpus and control criteria formulated as a sequence-l…

cs.LG2024

Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate

Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri +4

Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspectiv…

cs.AI2024

Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs

Elan Markowitz, Anil Ramakrishna, Jwala Dhamala +5

Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are ofte…

cs.CL2024

REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy

Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2

Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. For example, a higher p threshold in the…

cs.CL2024

Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs

Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates +5

Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences. A significant subset of UE methods utilize…