Search Results for author:
Found 17 papers, 7 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.
DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies
In this work, we define the novel problem of learning a TOD agent with dialog-KB inconsistencies in the training data.
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.
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses.
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.
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs
In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).
Unsupervised Learning of KB Queries in Task-Oriented Dialogs
Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses.
Mask & Focus: Conversation Modelling by Learning Concepts
The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts.
Unsupervised Learning of Interpretable Dialog Models
However these states need to be handcrafted and annotated in the data.
Multi-level Memory for Task Oriented Dialogs
Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results.
Disentangling Language and Knowledge in Task-Oriented Dialogs
We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model
To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph.
