Jinghong Chen

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Jinghong Chen

Jinghong Chen

The Six Elements of NLP Experiments. Part 1: Datasets

Experiments are the foundations of Natural Languge Processing (NLP). Just like experimental sciences, we test our proposed methods and verify our theories with experiments. Describing, planning, and conducting experiments are therefore the core skills of every NLP researcher. This article concerns describing NLP experiments. There are six basic elements that

Telling AI from Human: DeepFake Texts in the era of Large Language Models. Tutorial Notes @NAACL 2024

Telling AI from Human: DeepFake Texts in the era of Large Language Models. Tutorial Notes @NAACL 2024

This blog is NOT written or revised by AI. Human can be held responsible for what they write, whereas AI cannot. With capable language models such as ChatGPT, writing irresponsibly has become easier than ever. In the NAACL 2024 Tutorial by Adaku Uchendu et al. titled Catch Me if You

Control-DAG: Constraining Non-Autoregressive Text Generation with Weighted Finite State Automata (WFSA)

Control-DAG: Constraining Non-Autoregressive Text Generation with Weighted Finite State Automata (WFSA)

[4-minute read] TL;DR. Non-autoregressive (NAR) models generate texts much faster than auto-regresssive (AR) models. However, we find previous NAR approaches, largely developed for Machine Translation, fail harshly when faced with Task-Oriented Dialogue and Data-to-Text. Our NAACL 2024 paper introduces Control-DAG, a constrained decoding algorithm that uses Weighted Finite State

PreFLMR: SoTA Open-sourced Multi-modal Knowledge Retriever from Scaling Up FLMR

[1,087 words, 5-minute read] Three products emerged from our study in scaling up multi-modal late-interaction retrievers: * The Multi-task Multi-modal Knowledge Retrieval benchmark (M2KR) totaling 4.4M training examples for training and comprehensively evaluating knowledge retrievers on question-to-doc, image-to-doc, and question+image-to-doc tasks. * The Pretrained Fine-grained Late-interaction Multi-modal Retriever (PreFLMR)

Deriving Speculative Sampling Intuitively

[538 words, 2-minute read] A family of lossless LLM inference acceleration techniques has been developed based on speculative sampling (review here). Proposed by Google and Deepmind, speculative sampling is the following three-step procedure: 1. Draft: a small model (draft model, \(p(\cdot|\text{context})\)) quickly generates a K-token draft. 2.

Retrieval-Augmented Image Synthesis: A Researcher's Guide

[1029 words, 4-minute read] Retrieval-Augmented Image Synthesis (RA-IS) first retrieves relevant images from a database and then generates the image grounding in the retrieved images. RA-IS has been shown to (1) enhance image quality; (2) guide image styles; and (3) help faithfully generate specific objects (e.g., The Oriental Pearl

3-minute Pitch: Retrieval Guided Contrastive Learning for Hateful Memes Detection

[898 words, 3-minute read] Hateful memes are captioned images promoting hostility towards specific social groups. Most hateful memes detection systems are logistic classifiers built on the embedding space of pre-trained visual-langauge model (e.g., CLIP). However, we find that under these embedding spaces, hateful memes and belign memes are located