Search Results for author:
Found 27 papers, 4 papers with code
'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification
While existing work uses a static number of examples during inference for each data instance, in this paper we propose a novel methodology of dynamically adapting the number of examples as per the data.
Evaluating the Explainability of Neural Rankers
While the increasing complexity of the search models have been able to demonstrate improvements in effectiveness (measured in terms of relevance of top-retrieved results), a question worthy of a thorough inspection is - "how explainable are these models?
A Deep Learning Approach for Selective Relevance Feedback
Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries.
Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset
The recent success in language generation capabilities of large language models (LLMs), such as GPT, Bard, Llama etc., can potentially lead to concerns about their possible misuse in inducing mass agitation and communal hatred via generating fake news and spreading misinformation.
Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach
Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies.
Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation
Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations.
Query-specific Variable Depth Pooling via Query Performance Prediction towards Reducing Relevance Assessment Effort
However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries.
On the Feasibility and Robustness of Pointwise Evaluation of Query Performance Prediction
Despite the retrieval effectiveness of queries being mutually independent of one another, the evaluation of query performance prediction (QPP) systems has been carried out by measuring rank correlation over an entire set of queries.
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval.
Multi-Objective Few-shot Learning for Fair Classification
In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e. g., race, gender etc.).
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks
This means that an adversarial crafted image which is sufficiently close (visually indistinguishable) to its representative class can often be misclassified to be a member of a different class.
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics
Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers.
ALEX: Active Learning based Enhancement of a Model's Explainability
An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner.
Kernel Density Estimation based Factored Relevance Model for Multi-Contextual Point-of-Interest Recommendation
An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized 'points-of-interest' (POIs) to a user, if it can extract information from the user's preference history (exploitation) and effectively blend it with the user's current contextual information (exploration) to predict a POI's 'appropriateness' in the current context.
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning
Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice.
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports
Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports.
Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated Epidemics
In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations.
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult.
Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences
However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents.
Extracting Factual Min/Max Age Information from Clinical Trial Studies
Population age information is an essential characteristic of clinical trials.
Decision Conversations Decoded
We describe the vision and current version of a Natural Language Processing system aimed at group decision making facilitation.
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction
Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions.
Developing a Dataset for Evaluating Approaches for Document Expansion with Images
Motivated by the adage that a {``}picture is worth a thousand words{''} it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document.
