Recent efforts have incorporated large language models (LLMs) with exter...
Imitation Learning (IL) is one of the most widely used methods in machin...
Text generation under constraints have seen increasing interests in natu...
In-context learning (ICL) performs tasks by prompting a large language m...
Humans write code in a fundamentally interactive manner and rely on cons...
We propose Referral-Augmented Retrieval (RAR), a simple technique that
c...
Semantic textual similarity (STS) has been a cornerstone task in NLP tha...
Anthropomorphization is the tendency to attribute human-like traits to
n...
As language models increase in size by the day, methods for efficient
in...
Language models are increasingly being deployed for general problem solv...
Large language models (LLMs) have shown incredible capabilities and
tran...
Data multiplexing is a recently proposed method for improving a model's
...
Extreme classification (XC) involves predicting over large numbers of cl...
We consider the task of text generation in language models with constrai...
Fine-tuning pre-trained language models (PLMs) achieves impressive
perfo...
Multilingual pre-trained models exhibit zero-shot cross-lingual transfer...
While large language models (LLMs) have demonstrated impressive capabili...
Existing benchmarks for grounding language in interactive environments e...
Robust and generalized tool manipulation requires an understanding of th...
Strong inductive biases are a key component of human intelligence, allow...
A growing line of work has investigated the development of neural NLP mo...
We introduce CARETS, a systematic test suite to measure consistency and
...
In this paper, we propose Semantic Supervision (SemSup) - a unified para...
In this paper, we introduce data multiplexing (DataMUX), a technique tha...
Retrieving target videos based on text descriptions is a task of great
p...
Text adventure games present unique challenges to reinforcement learning...
While recent work on multilingual language models has demonstrated their...
Existing work in language grounding typically study single environments....
A major difficulty in debugging distributed systems lies in manually
det...
Despite their impressive performance in NLP, self-attention networks wer...
Text-based games simulate worlds and interact with players using natural...
In this paper, we consider the problem of leveraging textual description...
Cognitive control, the ability of a system to adapt to the demands of a ...
We propose a framework to integrate the concept of Theory of Mind (ToM) ...
In this paper, we tackle the problem of learning control policies for ta...
We consider the problem of learning control policies that optimize a rew...
Text-based games present a unique challenge for autonomous agents to ope...
In this paper, we propose a simple and effective technique to allow for
...
We explore unconstrained natural language feedback as a learning signal ...
Despite considerable progress, state of the art image captioning models
...
The ability to perform effective planning is crucial for building an
ins...
We consider the problem of reinforcement learning when provided with a
b...
Recent work has demonstrated the vulnerability of modern text classifier...
In the Vision-and-Language Navigation (VLN) task, an agent with egocentr...
We introduce a new algorithm for multi-objective reinforcement learning
...
Building accurate language models that capture meaningful long-term
depe...
While model-based deep reinforcement learning (RL) holds great promise f...
In this paper, we explore the utilization of natural language to drive
t...
The interpretation of spatial references is highly contextual, requiring...
This paper focuses on unsupervised modeling of morphological families,
c...