Search Results for author:
Found 15 papers, 11 papers with code
Few-Shot Recalibration of Language Models
Recent work has uncovered promising ways to extract well-calibrated confidence estimates from language models (LMs), where the model's confidence score reflects how likely it is to be correct.
On the Learnability of Watermarks for Language Models
Watermarking of language model outputs enables statistical detection of model-generated text, which has many applications in the responsible deployment of language models.
Benchmarking and Improving Generator-Validator Consistency of Language Models
To improve the consistency of LMs, we propose to finetune on the filtered generator and validator responses that are GV-consistent, and call this approach consistency fine-tuning.
Learning to Compress Prompts with Gist Tokens
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 the same prompt is computationally inefficient.
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
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).
Evaluating Human-Language Model Interaction
To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.
Contrastive Decoding: Open-ended Text Generation as Optimization
We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint.
Diffusion-LM Improves Controllable Text Generation
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation.
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation
When finetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff.
On the Opportunities and Risks of Foundation Models
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
Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks.
Decoding Methods for Neural Narrative Generation
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt.
Posterior Control of Blackbox Generation
In this work, we consider augmenting neural generation models with discrete control states learned through a structured latent-variable approach.
Specializing Word Embeddings (for Parsing) by Information Bottleneck
Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks.
A Generative Model for Punctuation in Dependency Trees
When the tree's yield is rendered as a written sentence, a string rewriting mechanism transduces the underlying marks into "surface" marks, which are part of the observed (surface) string but should not be regarded as part of the tree.
