Search Results for author:
Found 25 papers, 14 papers with code
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
In particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major performance boost over GPT-3 (and sometimes even over GPT-4).
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Here, we investigate an alternative that a priori seems impossible: can smaller language models (e. g., GPT-2) win over models that are orders of magnitude larger and better (e. g., GPT-3), if powered with novel commonsense distillation algorithms?
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Quark: Controllable Text Generation with Reinforced Unlearning
Large-scale language models often learn behaviors that are misaligned with user expectations.
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Many applications of text generation require incorporating different constraints to control the semantics or style of generated text.
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction.
Ranked #1 on
Text Generation
on ROCStories
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning.
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL.
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
Given a document and a target aspect (e. g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect.
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.
TuringAdvice: A Generative and Dynamic Evaluation of Language Use
We propose TuringAdvice, a new challenge task and dataset for language understanding models.
Social Bias Frames: Reasoning about Social and Power Implications of Language
We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others.
Counterfactual Story Reasoning and Generation
Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Deep Generative Models with Learnable Knowledge Constraints
The broad set of deep generative models (DGMs) has achieved remarkable advances.
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user engagement.
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition.
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem.
