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Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors

December 2024

Utilized dataset distillation and low-rank fine-tuning to enhance Small Language Models (SLMs) for simulating user agents in recommender systems. Our experiments provide compelling empirical evidence of the efficacy of our methods, demonstrating that user agents developed using our approach have the potential to bridge the gap between offline metrics and real-world performance of recommender systems.

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Enhancing Contract Negotiations with LLM-Based Legal Document Comparison

October 2024

This approach is the first in the literature to produce a natural language comparison between legal contracts and their template documents.

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Systematic Evaluation of Long-Context LLMs on Financial Concepts

October 2024

Evaluated the performance of state-of-the-art GPT-4 suite of LC LLMs in solving a series of progressively challenging tasks, as a function of factors such as context length, task difficulty, and position of key information by creating a real world financial news dataset.

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AR-NLU: A Framework for Enhancing Natural Language Understanding Model Robustness against ASR Errors

June 2024

A major challenge with pipeline spoken language understanding systems is that errors in the upstream automatic speech recognition (ASR) engine adversely impact downstream natural language understanding (NLU) models. To address this challenge, we propose an ASR-Robust NLU (AR-NLU) framework that extends a pre-existing NLU model by training it simultaneously on two input streams: human generated or gold transcripts and noisy ASR transcripts.

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When and how to paraphrase for named entity recognition?

May 2023

Utilized simple strategies to annotate entity spans in generations and compare established and novel methods of paraphrasing in NLP such as back translation, specialized encoder-decoder models such as Pegasus, and GPT-3 variants for their effectiveness in improving downstream performance for NER across different levels of gold annotations and paraphrasing strength on 5 datasets.

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Towards Data Efficient and Robust Speech Representation Model Distillation

November 2022

Our research aims to further improve the efficiency in task-agnostic speech representation model pre-training. By perturbing the training data distribution, we distil a more robust task-agnostic speech representation model with a lower training data requirement.

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EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

August 2022

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of {over-smoothing} and {under-reaching} to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, {mis-simplification}, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted.

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Model-based Reinforcement Learning for Predictions and Control for Limit Order Books

October 2019

We build a profitable electronic trading agent with Reinforcement Learning that places buy and sell orders in the stock market. We demonstrate that the trading policy trained entirely within the environment model can be transferred back into the real market and maintain its profitability.

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Towards Inverse Reinforcement Learning for Limit Order Book Dynamics

June 2019

This paper investigates whether IRL can infer such rewards from agents within real financial stochastic environments: limit order books (LOB).

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