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Found 20 papers, 5 papers with code
“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models
People use language in subtle and nuanced ways to convey their beliefs.
Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models
Current methods that train large language models (LLMs) with reinforcement learning feedback, often resort to averaging outputs of multiple rewards functions during training.
Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups
Our study shows that (1) there are significant differences across demographic groups (including intersectional groups) on how severe they assess the harm to be, and that these differences vary across different types of safety violations, (2) the diverse rater pool captures annotation patterns that are substantially different from expert raters trained on specific set of safety policies, and (3) the differences we observe in T2I safety are distinct from previously documented group level differences in text-based safety tasks.
Skill Generalization with Verbs
Our method allows a robot to generate a trajectory for a novel object based on a verb, which can then be used as input to a motion planner.
Steering Language Models with Game-Theoretic Solvers
Specifically, by modelling the players, strategies and payoffs in a "game" of dialogue, we create a binding from natural language interactions to the conventional symbolic logic of game theory.
Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches
People rely heavily on context to enrich meaning beyond what is literally said, enabling concise but effective communication.
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic \textit{partial} world model and policy that can be exploited by an RL agent.
Generalizing to New Domains by Mapping Natural Language to Lifted LTL
We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references.
Mapping Language Models to Grounded Conceptual Spaces
A fundamental criticism of text-only language models (LMs) is their lack of grounding---that is, the ability to tie a word for which they have learned a representation, to its actual use in the world.
Recognizing Multimodal Entailment
How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web.
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
We propose a vocabulary selection method that views words as members of a team trying to maximize the model{'}s performance.
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding
We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset.
Ranked #5 on
Vision and Language Navigation
on RxR
Robot Object Retrieval with Contextual Natural Language Queries
The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb.
On the Relationship Between Structure in Natural Language and Models of Sequential Decision Processes
Human language is distinguished by powerful semantics, rich structure, and incredible flexibility.
Planning with State Abstractions for Non-Markovian Task Specifications
Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction.
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling.
A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
We present a corpus of 5, 000 richly annotated abstracts of medical articles describing clinical randomized controlled trials.
Syntactic Patterns Improve Information Extraction for Medical Search
Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest.
