Search Results for author:
Found 22 papers, 12 papers with code
A picture is worth a thousand words: Using OpenClipArt library for enriching IndoWordNet
Our contribution is three fold: (1) We develop a system, which, given a synset in English, finds an appropriate image for the synset.
Diverse Preference Optimization
In this work we introduce Diverse Preference Optimization (DivPO), an optimization method which learns to generate much more diverse responses than standard pipelines, while maintaining the quality of the generations.
Adaptive Decoding via Latent Preference Optimization
During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate.
Towards Aligning Language Models with Textual Feedback
We present ALT (ALignment with Textual feedback), an approach that aligns language models with user preferences expressed in text.
Implicit Personalization in Language Models: A Systematic Study
This work systematically studies IP through a rigorous mathematical formulation, a multi-perspective moral reasoning framework, and a set of case studies.
A Diachronic Perspective on User Trust in AI under Uncertainty
In a human-AI collaboration, users build a mental model of the AI system based on its reliability and how it presents its decision, e. g. its presentation of system confidence and an explanation of the output.
Chain-of-Verification Reduces Hallucination in Large Language Models
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.
Variational Classification
We present a latent variable model for classification that provides a novel probabilistic interpretation of neural network softmax classifiers.
Extracting Victim Counts from Text
We cast victim count extraction as a question answering (QA) task with a regression or classification objective.
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference.
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.
Case-based Reasoning for Better Generalization in Textual Reinforcement Learning
Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency.
Deep Reinforcement Learning
Out-of-Distribution Generalization
+3
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents).
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.
A Simple Approach to Case-Based Reasoning in Knowledge Bases
We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI).
Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks
This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains.
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference
This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019).
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.
SlangNet: A WordNet like resource for English Slang
We present a WordNet like structured resource for slang words and neologisms on the internet.
