Search Results for author:
Found 31 papers, 15 papers with code
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc.
Information-Theoretic Distillation for Reference-less Summarization
The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models.
One Size Does Not Fit All: Customizing Open-Domain Procedures
Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need.
MacGyver: Are Large Language Models Creative Problem Solvers?
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting.
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Language technologies that accurately model the dynamics of events must perform commonsense reasoning.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
We further use the data generated by LINK to construct a dataset Logic-Induced-Long-Tail (LINT) that can be used to evaluate downstream models on the long-tail distribution; LINT contains 108K knowledge statements spanning four domains.
STEER: Unified Style Transfer with Expert Reinforcement
We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer.
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
To foster generalizable agent learning, we collect large-scale, unified, and high-quality training annotations derived from diverse ground-truth reasoning rationales across various complex interactive tasks.
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations
From this model we distill a high-quality dataset, \delta-Rules-of-Thumb, of 1. 2M entries of contextualizations and rationales for 115K defeasible moral actions rated highly by human annotators 85. 9% to 99. 8% of the time.
Affective and Dynamic Beam Search for Story Generation
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies.
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation.
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding.
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that itself cannot perform these tasks.
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finetuning.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
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 prompting can often be limited.
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
Towards Inter-character Relationship-driven Story Generation
In this paper, we introduce the task of modeling interpersonal relationships for story generation.
Generating Sequences by Learning to Self-Correct
Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content.
REV: Information-Theoretic Evaluation of Free-Text Rationales
More concretely, we propose a metric called REV (Rationale Evaluation with conditional V-information), to quantify the amount of new, label-relevant information in a rationale beyond the information already available in the input or the label.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge.
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.
Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation.
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.
Is Everything in Order? A Simple Way to Order Sentences
We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Cue Me In: Content-Inducing Approaches to Interactive Story Generation
Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user.
Modeling Protagonist Emotions for Emotion-Aware Storytelling
Emotions and their evolution play a central role in creating a captivating story.
