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Found 14 papers, 3 papers with code
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Large Language Models (LLMs) are known to exhibit social, demographic, and gender biases, often as a consequence of the data on which they are trained.
A Counterfactual Explanation Framework for Retrieval Models
However, limited attention has been given to understanding why a particular document is not favored (e. g. not within top-K) with respect to a query and a retrieval model.
Adaptive Retrieval-Augmented Generation for Conversational Systems
Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses.
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
We present and analyze a novel regularized form of the gradient clipping algorithm, proving that it converges to global minima of the loss surface of deep neural networks under the squared loss, provided that the layers are of sufficient width.
Automated Attribute Extraction from Legal Proceedings
The escalating number of pending cases is a growing concern world-wide.
Can Word Sense Distribution Detect Semantic Changes of Words?
Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora.
Lexical Entrainment for Conversational Systems
To address this, we propose a new dataset, named MULTIWOZ-ENTR, and a measure for LE for conversational systems.
Automated Argument Generation from Legal Facts
The count of pending cases has shown an exponential rise across nations (e. g., with more than 10 million pending cases in India alone).
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class
This data-efficiency is seen to manifest as LIPEx being able to compute its explanation matrix around 53% faster than all-class LIME, for classification experiments with text data.
Task2KB: A Public Task-Oriented Knowledge Base
Search engines and conversational assistants are commonly used to help users complete their every day tasks such as booking travel, cooking, etc.
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.).
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.
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.
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.
