In an information-seeking conversation, a user converses with an agent to ask a series of questions that can often be under- or over-specified.
Large-scale language models often learn behaviors that are misaligned with user expectations.
Large language models readily adapt to novel settings, even without task-specific training data.
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.
This paper presents a new benchmark, ScienceWorld, to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.
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.
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.
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.
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.
We seek to create agents that both act and communicate with other agents in pursuit of a goal.
In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning.
Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards.
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.
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.
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language.
Text-adventure games are typically structured as puzzles or quests wherein the player must execute certain actions in a certain order to succeed.
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.
Ranked #1 on Event Expansion on Scifi TV Shows
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.
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).
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.
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).