Search Results for author:
Found 11 papers, 4 papers with code
Assessing Large Language Models on Climate Information
We evaluate several recent LLMs and conduct a comprehensive analysis of the results, shedding light on both the potential and the limitations of LLMs in the realm of climate communication.
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
The predictions of question answering (QA)systems are typically evaluated against manually annotated finite sets of one or more answers.
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.
Fool Me Twice: Entailment from Wikipedia Gamification
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game.
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims.
Meta Answering for Machine Reading
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.
Multi-agent query reformulation: Challenges and the role of diversity
We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation
We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering.
Analyzing Language Learned by an Active Question Answering Agent
We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017].
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.
