Search Results for author:
Found 15 papers, 4 papers with code
DiverseAgentEntropy: Quantifying Black-Box LLM Uncertainty through Diverse Perspectives and Multi-Agent Interaction
In this paper, we propose a novel method, DiverseAgentEntropy, for evaluating a model's uncertainty using multi-agent interaction under the assumption that if a model is certain, it should consistently recall the answer to the original query across a diverse collection of questions about the same original query.
Inference time LLM alignment in single and multidomain preference spectrum
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure.
Open Domain Question Answering with Conflicting Contexts
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
Task-Specific Embeddings for Ante-Hoc Explainable Text Classification
In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions.
Enhancing Product Safety in E-Commerce with NLP
Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Task-Aware Representation of Sentences for Generic Text Classification
State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem.
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
We introduce a deep learning model to learn the set of enumerated job skills associated with a job description.
Modeling Information Need of Users in Search Sessions
Users issue queries to Search Engines, and try to find the desired information in the results produced.
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture
Users participate in online discussion forums to learn from others and share their knowledge with the community.
Treatment Side Effect Prediction from Online User-generated Content
With Health 2. 0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities.
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it.
Sentiment Analysis for Twitter : Going Beyond Tweet Text
Analysing sentiment of tweets is important as it helps to determine the users' opinion.
