Search Results for author:
Found 23 papers, 11 papers with code
A Corpus for Reasoning About Natural Language Grounded in Photographs
We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language.
NLVR2 Visual Bias Analysis
We show that the performance of existing models (Li et al., 2019; Tan and Bansal 2019) is relatively robust to this potential bias.
What's In My Big Data?
We open-source WIMBD's code and artifacts to provide a standard set of evaluations for new text-based corpora and to encourage more analyses and transparency around them.
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task.
Ranked #10 on
Vision and Language Navigation
on Touchdown Dataset
We're Afraid Language Models Aren't Modeling Ambiguity
We find that the task remains extremely challenging, including for GPT-4, whose generated disambiguations are considered correct only 32% of the time in human evaluation, compared to 90% for disambiguations in our dataset.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
We propose a context-dependent model to map utterances within an interaction to executable formal queries.
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
In this work, we focus on LLM sensitivity to a quintessential class of meaning-preserving design choices: prompt formatting.
Autonomous Evaluation and Refinement of Digital Agents
We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control.
Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation
We propose a learning approach for mapping context-dependent sequential instructions to actions.
Visual Reasoning with Natural Language
To understand language in complex environments, agents must reason about the full range of language inputs and their correspondence to the world.
Neural Semantic Parsing
Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation.
A Corpus of Natural Language for Visual Reasoning
We present a new visual reasoning language dataset, containing 92, 244 pairs of examples of natural statements grounded in synthetic images with 3, 962 unique sentences.
Executing Instructions in Situated Collaborative Interactions
We study a collaborative scenario where a user not only instructs a system to complete tasks, but also acts alongside it.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training.
Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior
We study continual learning for natural language instruction generation, by observing human users' instruction execution.
Analysis of Language Change in Collaborative Instruction Following
We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise.
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers.
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines.
Continual Learning for Instruction Following from Realtime Feedback
We propose and deploy an approach to continually train an instruction-following agent from feedback provided by users during collaborative interactions.
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world.
Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
We present SymbolicToM, a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation.
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Language technologies that accurately model the dynamics of events must perform commonsense reasoning.
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
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).
