2.3k citations · 3k across the 11 of their papers we have counts for
14 papers
Exchanging-based Multimodal Fusion with Transformer
Renyu Zhu, Chengcheng Han, Yong Qian +5
We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from…
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Qiushi Sun, Chengcheng Han, Nuo Chen +4
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbit…
Learning to Compress Prompts with Gist Tokens
Jesse Mu, Xiang Lisa Li, Noah Goodman
Prompting is the primary way to utilize the multitask capabilities of language models (LMs), but prompts occupy valuable space in the input context window, and repeatedly encoding…
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Omar Khattab, Keshav Santhanam, Xiang Lisa Li +4
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Exi…
Evaluating Human-Language Model Interaction
Mina Lee, Megha Srivastava, Amelia Hardy +15
Many real-world applications of language models (LMs), such as writing assistance and code autocomplete, involve human-LM interaction. However, most benchmarks are non-interactive…
Contrastive Decoding: Open-ended Text Generation as Optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried +5
Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling…
Diffusion-LM Improves Controllable Text Generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani +2
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on cont…
TempLM: Distilling Language Models into Template-Based Generators
Tianyi Zhang, Mina Lee, Lisa Li +2
While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic templa…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li, Percy Liang
Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks. However, it modifies all the language model parameters and therefore neces…
Decoding Methods for Neural Narrative Generation
Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li +1
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovatio…
Posterior Control of Blackbox Generation
Xiang Lisa Li, Alexander M. Rush
Text generation often requires high-precision output that obeys task-specific rules. This fine-grained control is difficult to enforce with off-the-shelf deep learning models. In t…
Specializing Word Embeddings (for Parsing) by Information Bottleneck
Xiang Lisa Li, Jason Eisner
Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast v…
A Generative Model for Punctuation in Dependency Trees
Xiang Lisa Li, Dingquan Wang, Jason Eisner
Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree's "true" punctuation marks are not observed (Nunberg, 1990). These laten…