Search Results for author:
Found 10 papers, 5 papers with code
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
In this paper, we propose a modified version of the MedQA-USMLE dataset, which is subjective, to mimic real-life clinical scenarios.
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner.
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
PMI quantifies the extent to which the document influences the generated response -- with a higher PMI indicating a more faithful response.
A Solver-Free Framework for Scalable Learning in Neural ILP Architectures
Experiments on several problems, both perceptual as well as symbolic, which require learning the constraints of an ILP, show that our approach has superior performance and scales much better compared to purely neural baselines and other state-of-the-art models that require solver-based training.
Matching Papers and Reviewers at Large Conferences
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper.
Neural Models for Output-Space Invariance in Combinatorial Problems
One drawback of the proposed architectures, which are often based on Graph Neural Networks (GNN), is that they cannot generalize across the size of the output space from which variables are assigned a value, for example, set of colors in a GCP, or board-size in sudoku.
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion
Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world.
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces
Our framework uses a selection module, whose goal is to dynamically determine, for every input, the solution that is most effective for training the network parameters in any given learning iteration.
OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion
State-of-the-art models for Knowledge Base Completion (KBC) are based on tensor factorization (TF), e. g, DistMult, ComplEx.
A Primal Dual Formulation For Deep Learning With Constraints
In this paper, we present a constrained optimization formulation for training a deep network with a given set of hard constraints on output labels.
