Search Results for author:
Found 11 papers, 4 papers with code
The CRINGE Loss: Learning what language not to model
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples.
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Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice.
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model BlenderBot 2 (Chen et al., 2021) on open-domain knowledge-grounded conversations for the same number of parameters, in terms of consistency, knowledge and per-turn engagingness.
Calibration of Machine Reading Systems at Scale
We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings.
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies.
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.
How to Query Language Models?
We apply this approach of querying by example to the LAMA probe and obtain substantial improvements of up to 37. 8% for BERT-large on the T-REx data when providing only 10 demonstrations--even outperforming a baseline that queries the model with up to 40 paraphrases of the question.
Ellipsoidal Trust Region Methods for Neural Network Training
We investigate the use of ellipsoidal trust region constraints for second-order optimization of neural networks.
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games
We, however, consider the task of designing an agent that not just succeeds in a single game, but performs well across a whole family of games, sharing the same theme.
Adaptive norms for deep learning with regularized Newton methods
We investigate the use of regularized Newton methods with adaptive norms for optimizing neural networks.
Local Saddle Point Optimization: A Curvature Exploitation Approach
Gradient-based optimization methods are the most popular choice for finding local optima for classical minimization and saddle point problems.
