Search Results for author: Prithviraj Ammanabrolu

Found 33 papers, 18 papers with code

Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

no code implementations2 Jun 2023 Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, Hannaneh Hajishirzi

We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e. g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e. g., factual incorrectness, irrelevance, and information incompleteness).

Language Modelling Long Form Question Answering +1

SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

1 code implementation27 May 2023 Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, Xiang Ren

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.

Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement Learning

1 code implementation CVPR 2023 Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, Yejin Choi

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e. g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 manifest broad commonsense reasoning capacity.

Language Modelling reinforcement-learning +2

Behavior Cloned Transformers are Neurosymbolic Reasoners

1 code implementation13 Oct 2022 Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu

In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation.

Common Sense Reasoning

Aligning to Social Norms and Values in Interactive Narratives

no code implementations NAACL 2022 Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap, Hannaneh Hajishirzi, Yejin Choi

We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language.

text-based games

ScienceWorld: Is your Agent Smarter than a 5th Grader?

1 code implementation14 Mar 2022 Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu

We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.

Question Answering

Inherently Explainable Reinforcement Learning in Natural Language

1 code implementation16 Dec 2021 Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu

We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud while performing a task and analyzing entire trajectories post-hoc to produce causal explanations.

Graph Attention reinforcement-learning +1

Situated Dialogue Learning through Procedural Environment Generation

no code implementations ACL 2022 Prithviraj Ammanabrolu, Renee Jia, Mark O. Riedl

We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums.

Modeling Worlds in Text

1 code implementation AKBC Workshop CSKB 2021 Prithviraj Ammanabrolu, Mark O. Riedl

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives.

Knowledge Graphs Question Answering

Telling Stories through Multi-User Dialogue by Modeling Character Relations

no code implementations SIGDIAL (ACL) 2021 Wai Man Si, Prithviraj Ammanabrolu, Mark O. Riedl

This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue -- requiring models to select language that is consistent with a character's persona and their relationships with other characters while following and advancing the story.

Story Continuation

Situated Language Learning via Interactive Narratives

no code implementations18 Mar 2021 Prithviraj Ammanabrolu, Mark O. Riedl

This paper provides a roadmap that explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language in service of achieving a goal.

Decision Making

Playing Text-Based Games with Common Sense

no code implementations4 Dec 2020 Sahith Dambekodi, Spencer Frazier, Prithviraj Ammanabrolu, Mark O. Riedl

We test our technique in the 9to05 game, which is an extreme version of a text based game that requires numerous interactions with common, everyday objects in common, everyday scenarios.

Common Sense Reasoning Language Modelling +1

Automated Storytelling via Causal, Commonsense Plot Ordering

1 code implementation2 Sep 2020 Prithviraj Ammanabrolu, Wesley Cheung, William Broniec, Mark O. Riedl

In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning.

How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds

1 code implementation12 Jun 2020 Prithviraj Ammanabrolu, Ethan Tien, Matthew Hausknecht, Mark O. Riedl

Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards.

text-based games

How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents

1 code implementation19 Feb 2020 Prithviraj Ammanabrolu, Ethan Tien, Zhaochen Luo, Mark O. Riedl

We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1, where prior agent have been unable to get past a bottleneck where the agent is eaten by a Grue.

Knowledge Graphs reinforcement-learning +2

Bringing Stories Alive: Generating Interactive Fiction Worlds

1 code implementation28 Jan 2020 Prithviraj Ammanabrolu, Wesley Cheung, Dan Tu, William Broniec, Mark O. Riedl

This knowledge graph is then automatically completed utilizing thematic knowledge and used to guide a neural language generation model that fleshes out the rest of the world.

Text Generation

Toward Automated Quest Generation in Text-Adventure Games

no code implementations CCNLG (ACL) 2019 Prithviraj Ammanabrolu, William Broniec, Alex Mueller, Jeremy Paul, Mark O. Riedl

Text-adventure games are typically structured as puzzles or quests wherein the player must execute certain actions in a certain order to succeed.

Interactive Fiction Games: A Colossal Adventure

2 code implementations11 Sep 2019 Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, Xingdi Yuan

A hallmark of human intelligence is the ability to understand and communicate with language.

Story Realization: Expanding Plot Events into Sentences

1 code implementation8 Sep 2019 Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara J. Martin, Mark O. Riedl

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries.

Event Expansion Story Generation

Transfer in Deep Reinforcement Learning using Knowledge Graphs

no code implementations WS 2019 Prithviraj Ammanabrolu, Mark O. Riedl

Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language.

Knowledge Graphs Question Answering +3

Guided Neural Language Generation for Automated Storytelling

no code implementations WS 2019 Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara Martin, Mark Riedl

Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence).

Story Generation

Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

2 code implementations NAACL 2019 Prithviraj Ammanabrolu, Mark O. Riedl

Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language.

Efficient Exploration Question Answering +3

Event Representations for Automated Story Generation with Deep Neural Nets

1 code implementation5 Jun 2017 Lara J. Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, Mark O. Riedl

We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence).

Event Expansion Story Generation +1

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