activity
20172024
most citedAutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

69 citations · 106 across the 12 of their papers we have counts for

collaborators

15 papers

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

CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints

Anirudh Atmakuru, Jatin Nainani, Rohith Siddhartha Reddy Bheemreddy +4

Evaluating the creativity of large language models (LLMs) in story writing is difficult because LLM-generated stories could seemingly look creative but be very similar to some exis…

cs.CL2024

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin +7

Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to…

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.IR2023

To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders

Haw-Shiuan Chang, Nikhil Agarwal, Andrew McCallum

Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is…

cs.CL20231 cited

Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens

Ronald Seoh, Haw-Shiuan Chang, Andrew McCallum

Many useful tasks on scientific documents, such as topic classification and citation prediction, involve corpora that span multiple scientific domains. Typically, such tasks are ac…