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Found 42 papers, 21 papers with code
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions.
Language-guided Skill Learning with Temporal Variational Inference
We present an algorithm for skill discovery from expert demonstrations.
Policy Improvement using Language Feedback Models
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following.
V-STaR: Training Verifiers for Self-Taught Reasoners
Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on self-generated solutions to improve their problem-solving ability.
Guiding Language Model Math Reasoning with Planning Tokens
Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought reasoning.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer.
Augmenting Autotelic Agents with Large Language Models
Without relying on any hand-coded goal representations, reward functions or curriculum, we show that LMA3 agents learn to master a large diversity of skills in a task-agnostic text-based environment.
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks.
Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding
In this study, we explored the use of large language models (LLMs) in supporting deductive coding, a major category of qualitative analysis where researchers use pre-determined codebooks to label the data into a fixed set of codes.
A Song of Ice and Fire: Analyzing Textual Autotelic Agents in ScienceWorld
We show the importance of selectivity from the social peer's feedback; that experience replay needs to over-sample examples of rare goals; and that following self-generated goal sequences where the agent's competence is intermediate leads to significant improvements in final performance.
GPT-3-driven pedagogical agents for training children's curious question-asking skills
In this context, we propose to leverage advances in the natural language processing field (NLP) and investigate the efficiency of using a large language model (LLM) for automating the production of the pedagogical content of a curious question-asking (QA) training.
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation.
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain.
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
In order to study how agents can be taught to query external knowledge via language, we first introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld.
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
Interactive Machine Comprehension with Dynamic Knowledge Graphs
Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Faceted summarization provides briefings of a document from different perspectives.
Ranked #1 on
Unsupervised Extractive Summarization
on FacetSum
BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning
ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
Graph Policy Network for Transferable Active Learning on Graphs
Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields.
Role-Wise Data Augmentation for Knowledge Distillation
To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Playing text-based games requires skills in processing natural language and sequential decision making.
Building Dynamic Knowledge Graphs from Text-based Games
We are interested in learning how to update Knowledge Graphs (KG) from text.
Interactive Fiction Games: A Colossal Adventure
A hallmark of human intelligence is the ability to understand and communicate with language.
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.
Interactive Language Learning by Question Answering
In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions.
Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA).
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens.
Towards Solving Text-based Games by Producing Adaptive Action Spaces
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans.
Ranked #3 on
Procedural Text Understanding
on ProPara
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
Neural Models for Key Phrase Extraction and Question Generation
We propose a two-stage neural model to tackle question generation from documents.
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
Counting to Explore and Generalize in Text-based Games
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.
Rapid Adaptation with Conditionally Shifted Neurons
We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons.
Neural Models for Key Phrase Detection and Question Generation
We propose a two-stage neural model to tackle question generation from documents.
A Joint Model for Question Answering and Question Generation
We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents.
Machine Comprehension by Text-to-Text Neural Question Generation
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.
NewsQA: A Machine Comprehension Dataset
We present NewsQA, a challenging machine comprehension dataset of over 100, 000 human-generated question-answer pairs.
Natural Language Comprehension with the EpiReader
We present the EpiReader, a novel model for machine comprehension of text.
Ranked #7 on
Question Answering
on Children's Book Test
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set.
Ranked #1 on
Question Answering
on MCTest-160
