Search Results for author:
Found 18 papers, 4 papers with code
Assessing Large Language Models on Climate Information
As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains.
In-Context Probing: Toward Building Robust Classifiers via Probing Large Language Models
However, the effectiveness of in-context learning is dependent on the provided context, and the performance on a downstream task can vary considerably, depending on the instruction.
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.
Decoding a Neural Retriever's Latent Space for Query Suggestion
Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.
Zero-Shot Retrieval with Search Agents and Hybrid Environments
We extend the previous learning to search setup to a hybrid environment, which accepts discrete query refinement operations, after a first-pass retrieval step via a dual encoder.
Boosting Search Engines with Interactive Agents
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks.
ClimaText: A Dataset for Climate Change Topic Detection
Climate change communication in the mass media and other textual sources may affect and shape public perception.
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.
On Identifiability in Transformers
We show that, for sequences longer than the attention head dimension, attention weights are not identifiable.
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.
Zero-Shot Dual Machine Translation
Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available.
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.
