Paper Citation Record · LEDGER

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 9 inbound Pith citation observations for arXiv:2411.09947.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.09947 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:11:46.653735Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:54:34.550318Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T06:55:59.276055Z

Reference resolution

45 of 45 outbound references displayed

verified exact2
verified fuzzy19
unresolved24
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c583d6e0-864f-4be2-8f11-fa130d1af553 · outbound

This paper cites An overview of chatbot technology.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning An overview of chatbot technology

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.772152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4813ddfd-10c2-46b8-ae37-48250f65a378 · outbound

This paper cites Conversational agents in healthcare: a systematic review.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Conversational agents in healthcare: a systematic review

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.753751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 82bbcf05-02cb-46de-800e-6ff36afcec79 · outbound

This paper cites Unleashing the potential of chatbots in education: A state-of-the-art analysis.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Unleashing the potential of chatbots in education: A state-of-the-art analysis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.734833Z

Source-reported events for the cited work

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Observation 377d8f89-7f31-4417-963d-0a6d1389ef18 · outbound

This paper cites Neural approaches to conversational ai.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Neural approaches to conversational ai

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.717100Z

Source-reported events for the cited work

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Observation 4d8c66ac-a00d-4991-a1a4-316c8c3af131 · outbound

This paper cites Learning from Dialogue after Deployment: Feed Yourself, Chatbot!.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

Reference 5

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Source-reported events for the cited work

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Observation b7e9b645-79b3-403f-a8a9-3a4f032d0c98 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Fine-Tuning Language Models from Human Preferences

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T20:11:46.439735Z

Source-reported events for the cited work

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Observation 4666a5fe-7dc3-4649-a747-c868bc84282b · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning A reduction of imitation learning and structured prediction to no-regret online learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.698294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 644c630d-c090-41c8-86ae-1c5f27ab76e3 · outbound

This paper cites Dialog-based language learning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Dialog-based language learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.679924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0ca59fe9-7148-4d99-9aa8-c45c5ee4656e · outbound

This paper cites Deep reinforcement learning from human prefer- ences.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Deep reinforcement learning from human prefer- ences

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.663128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd182ba2-6d41-4ecc-8530-58df5f1584ea · outbound

This paper cites Training language models to follow instructions with human feedback.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Training language models to follow instructions with human feedback

Reference 10

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Source-reported events for the cited work

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Observation d31dff68-3f67-488a-aac3-71eb787cffc6 · outbound

This paper cites A contrastive deep learning approach to cryptocurrency portfolio with us treasuries.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning A contrastive deep learning approach to cryptocurrency portfolio with us treasuries

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.631998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0e025321-6bf2-4e87-9b4f-5357a3fa3cdd · outbound

This paper cites GPT-4 Technical Report.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning GPT-4 Technical Report

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05d6fbda-7141-4cdd-9416-f7ba90c68086 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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Source-reported events for the cited work

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Observation 48095b9d-3a9f-412f-ad92-2fdc250272d3 · outbound

This paper cites Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Reference 14

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Source-reported events for the cited work

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Observation 1fb6b168-4fa2-40be-882e-48bd3e5575a8 · outbound

This paper cites Incorporating economic indicators and market sentiment effect into us treasury bond yield prediction with machine learning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Incorporating economic indicators and market sentiment effect into us treasury bond yield prediction with machine learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.614601Z

Source-reported events for the cited work

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Observation 90c8cea8-bd2c-47f0-9de3-39196dc82a82 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning LLaMA: Open and Efficient Foundation Language Models

Reference 16

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Source-reported events for the cited work

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Observation 0472a396-e900-4835-b5b1-74cff64fd7e4 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Gemma 2: Improving Open Language Models at a Practical Size

Reference 17

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Source-reported events for the cited work

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Observation 25c91759-2dcf-4eaf-b53b-a53af4df5e4c · outbound

This paper cites RATT: A Thought Structure for Coherent and Correct LLM Reasoning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning RATT: A Thought Structure for Coherent and Correct LLM Reasoning

Reference 18

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Source-reported events for the cited work

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Observation eedd649d-02aa-4695-a456-0a44e741cec2 · outbound

This paper cites Thought space explorer: Navigating and expanding thought space for large language model reasoning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Thought space explorer: Navigating and expanding thought space for large language model reasoning

Reference 19

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Source-reported events for the cited work

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Observation d1897f40-4759-4719-ab6d-d78fcaf433f5 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Direct preference optimization: Your language model is secretly a reward model

Reference 20

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Source-reported events for the cited work

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Observation a82889d2-93c7-4e35-886a-517e84ae2761 · outbound

This paper cites In-context time series predictor.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning In-context time series predictor

Reference 21

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Observation f6cd7e1a-9343-497b-9acd-a8454147d297 · outbound

This paper cites Utilizing Large Language Models for Information Extraction from Real Estate Transactions.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Utilizing Large Language Models for Information Extraction from Real Estate Transactions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T20:11:46.524211Z

Source-reported events for the cited work

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Observation 4d4b316b-afd7-451a-a4fb-5500ca73e3e1 · outbound

This paper cites Using large language models in real estate transactions: A few-shot learning approach.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Using large language models in real estate transactions: A few-shot learning approach

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.588418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ffafc69c-8af1-4a3d-8b6d-66d571f1a958 · outbound

This paper cites BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra

Reference 24

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Source-reported events for the cited work

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Observation bb198edd-afb2-423c-a67b-0e749262e6ed · outbound

This paper cites Accurate training of web-based question answering systems with feedback from ranked users.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Accurate training of web-based question answering systems with feedback from ranked users

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.572923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b028d99-7be8-4f40-b275-48fdc9cff3e8 · outbound

This paper cites Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis

Reference 26

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5cec7fbf-9c82-4b82-9de4-5c917fe424f2 · outbound

This paper cites Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning

Reference 27

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Observation e2b7aada-30b1-4280-882a-2798f01646aa · outbound

This paper cites Can speculative sampling accelerate react without compromising reasoning quality? In The Second Tiny Papers Track at ICLR 2024.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Can speculative sampling accelerate react without compromising reasoning quality? In The Second Tiny Papers Track at ICLR 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.557442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6557637-c5ff-4491-9f99-ee93eee4422a · outbound

This paper cites CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing

Reference 29

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Source-reported events for the cited work

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Observation 2676466d-399b-4a03-a098-f2774b330387 · outbound

This paper cites Integrated optimization of large language models: Synergizing data utilization and compression techniques.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Integrated optimization of large language models: Synergizing data utilization and compression techniques

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.539822Z

Source-reported events for the cited work

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Observation deacd70e-1dad-44c9-8b98-7040baef49ac · outbound

This paper cites Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation

Reference 31

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This paper cites Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness

Reference 32

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Source-reported events for the cited work

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Observation 2c6310b7-d4c6-4cee-ae6b-934473f91c39 · outbound

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Towards Federated Learning at Scale: System Design

Reference 33

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Reference 34

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This paper cites Pmfl: Partial meta-federated learning for heterogeneous tasks and its applications on real-world medical records.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Pmfl: Partial meta-federated learning for heterogeneous tasks and its applications on real-world medical records

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.520464Z

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Observation 579effa3-93e8-4779-a43f-e711da416900 · outbound

This paper cites Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

Reference 36

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Observation 9c1f1fa6-30a1-4b1c-b18e-a89090ec9d10 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 37

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 38

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Ensemble methods in machine learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.501976Z

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Observation bec5c858-8421-4b82-a0cc-26fc054141d4 · outbound

This paper cites Restful- llama: Connecting user queries to restful apis.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Restful- llama: Connecting user queries to restful apis

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.483987Z

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Observation 976fc49e-3871-49b2-b3f0-ae5ae854e0a6 · outbound

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Dual learning for machine translation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.466747Z

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Observation 60515112-a7dd-4912-bef9-3371790bb408 · outbound

This paper cites An ensemble approach to stock price prediction using deep learning and time series models.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning An ensemble approach to stock price prediction using deep learning and time series models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.445350Z

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Observation b217f65e-3e74-4154-9ebe-17c7370ee81f · outbound

This paper cites Meta learning enabled adversarial defense.

LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Meta learning enabled adversarial defense

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:11:47.426161Z

Source-reported events for the cited work

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Observation 4700cc71-cdca-4c49-a293-1d1f814ea8d9 · outbound

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning Steerdiff: Steering towards safe text-to-image diffusion models

Reference 44

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LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training

Reference 45

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Pith citing papers

Observation efa39af7-6918-407a-9677-f2ba5abc1ecb · inbound

Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models cites this paper.

Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 14

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Observation 32162529-e39c-471d-a668-781dcbfe6688 · inbound

Accurate Medical Named Entity Recognition Through Specialized NLP Models cites this paper.

Accurate Medical Named Entity Recognition Through Specialized NLP Models LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 18

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Observation a103e032-4766-43a0-87a5-2ba1ae22aa4b · inbound

Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications cites this paper.

Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 13

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Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer cites this paper.

Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 5

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Observation c6bb979f-27c9-43f1-9e99-8ae7170ba928 · inbound

Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks cites this paper.

Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 12

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Observation 551fe730-d7d2-4466-97dc-504b48c619d4 · inbound

Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models cites this paper.

Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T00:37:46.421790Z

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Observation 5f138ca8-dc92-4867-8be0-b356225de79b · inbound

Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data cites this paper.

Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:26:39.832740Z

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Observation 2a6ce578-0846-4955-b71b-d2eb69a82691 · inbound

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models cites this paper.

Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:55:04.100403Z

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Observation 9e895f02-26b7-4bfc-aad3-c8bff5b8b1fa · inbound

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models cites this paper.

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:55:59.280114Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.