565 citations · 784 across the 20 of their papers we have counts for
21 papers
MacGyver: Are Large Language Models Creative Problem Solvers?
Yufei Tian, Abhilasha Ravichander, Lianhui Qin +6
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting o…
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Ximing Lu, Faeze Brahman, Peter West +14
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure promptin…
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula, Jena D. Hwang, Doug Downey +7
Commonsense capabilities of pre-trained language models dramatically improve with scale, leading many to believe that scale is the only winning recipe. But is it? Here, we investig…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Quark: Controllable Text Generation with Reinforced Unlearning
Ximing Lu, Sean Welleck, Jack Hessel +5
Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition…
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Jaehun Jung, Lianhui Qin, Sean Welleck +4
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide…
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
Wenhao Yu, Chenguang Zhu, Lianhui Qin +3
Generative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text. Recent years have seen a surge of interest in improvin…
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi +1
Many applications of text generation require incorporating different constraints to control the semantics or style of generated text. These constraints can be hard (e.g., ensuring…
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, Sean Welleck, Peter West +9
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constra…
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Daniel Khashabi, Shane Lyu, Sewon Min +8
Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the fe…
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin, Aditya Gupta, Shyam Upadhyay +3
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progr…
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
Bowen Tan, Lianhui Qin, Eric P. Xing +1
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. Previous studies…
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Lianhui Qin, Vered Shwartz, Peter West +5
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple conte…
TuringAdvice: A Generative and Dynamic Evaluation of Language Use
Rowan Zellers, Ari Holtzman, Elizabeth Clark +3
We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate…
Social Bias Frames: Reasoning about Social and Power Implications of Language
Maarten Sap, Saadia Gabriel, Lianhui Qin +3
Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the c…
Counterfactual Story Reasoning and Generation
Lianhui Qin, Antoine Bosselut, Ari Holtzman +3
Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a neces…
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Lianhui Qin, Michel Galley, Chris Brockett +5
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 contentf…
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu, Haoran Shi, Bowen Tan +12
We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, su…
Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov +4
The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end…
Automatic Article Commenting: the Task and Dataset
Lianhui Qin, Lemao Liu, Victoria Bi +5
Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent ch…
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Lianhui Qin, Zhisong Zhang, Hai Zhao +2
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…