{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/parameter-efficient-fine-tuning/papers/8","list_of":"/task/parameter-efficient-fine-tuning","task":"parameter-efficient fine-tuning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":10,"rows_per_page":100,"rows":[701,800],"of":935,"counts":{"archive_papers_tagged":935,"with_a_code_link":441,"where_syntology_ran_a_sample":198,"not_listed_spam_title":0,"listed":935,"listed_where_code_ran":198,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":176,"every_run_a_failure_of_syntologys_instrument":22,"listed_with_a_run_with_no_instrument_failure":176,"listed_every_run_a_failure_of_syntologys_instrument":22,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/parameter-efficient-fine-tuning","prev":"/task/parameter-efficient-fine-tuning/papers/7","next":"/task/parameter-efficient-fine-tuning/papers/9","papers":[{"url":null,"slug":"from-text-to-emoji-how-peft-driven","title":"From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs","date":"2024-09-16","arxiv_id":"2409.10245","repositories_listed":0,"syntology":null},{"url":null,"slug":"comfort-a-continual-fine-tuning-framework-for","title":"COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare","date":"2024-09-14","arxiv_id":"2409.09549","repositories_listed":0,"syntology":null},{"url":null,"slug":"risks-when-sharing-lora-fine-tuned-diffusion","title":"Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights","date":"2024-09-13","arxiv_id":"2409.08482","repositories_listed":0,"syntology":null},{"url":null,"slug":"svfit-parameter-efficient-fine-tuning-of","title":"SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values","date":"2024-09-09","arxiv_id":"2409.05926","repositories_listed":0,"syntology":null},{"url":null,"slug":"deconfounded-causality-aware-parameter","title":"Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs","date":"2024-09-04","arxiv_id":"2409.02686","repositories_listed":0,"syntology":null},{"url":null,"slug":"iconformer-dynamic-parameter-efficient-tuning","title":"iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation","date":"2024-09-04","arxiv_id":"2409.02838","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-finetuning-of-foundation","title":"Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA","date":"2024-09-04","arxiv_id":"2409.02346","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-hybrid-parameter-efficient-fine","title":"A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis","date":"2024-09-02","arxiv_id":"2409.00884","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-specific-dialogue-generation-with-user","title":"User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning","date":"2024-09-02","arxiv_id":"2409.00887","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedmcp-parameter-efficient-federated-learning","title":"FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization","date":"2024-08-28","arxiv_id":"2409.00116","repositories_listed":0,"syntology":null},{"url":"/paper/scaling-up-summarization-leveraging-large","slug":"scaling-up-summarization-leveraging-large","title":"Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization","date":"2024-08-28","arxiv_id":"2408.15801","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-everywhere-parameter-efficient","title":"Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training","date":"2024-08-27","arxiv_id":"2408.15011","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-enterprise-spatio-temporal","title":"Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings","date":"2024-08-24","arxiv_id":"2408.13622","repositories_listed":0,"syntology":null},{"url":null,"slug":"offline-policy-learning-via-skill-step","title":"Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks","date":"2024-08-21","arxiv_id":"2408.11300","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-interchangeability-of-positional","title":"Towards Inducing Document-Level Abilities in Standard Multilingual Neural Machine Translation Models","date":"2024-08-21","arxiv_id":"2408.11382","repositories_listed":0,"syntology":null},{"url":null,"slug":"pluto-and-charon-a-time-and-memory-efficient","title":"Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-Tuning","date":"2024-08-20","arxiv_id":"2408.10746","repositories_listed":0,"syntology":null},{"url":null,"slug":"combo-co-speech-holistic-3d-human-motion","title":"Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony","date":"2024-08-18","arxiv_id":"2408.09397","repositories_listed":0,"syntology":null},{"url":null,"slug":"mergerepair-an-exploratory-study-on-merging","title":"MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair","date":"2024-08-18","arxiv_id":"2408.09568","repositories_listed":0,"syntology":null},{"url":null,"slug":"nora-nested-low-rank-adaptation-for-efficient","title":"NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models","date":"2024-08-18","arxiv_id":"2408.10280","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-chinese-landscape-paintings-generation","title":"A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBooth","date":"2024-08-16","arxiv_id":"2408.08561","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-layer-selection-for-efficient-vision","title":"Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning","date":"2024-08-16","arxiv_id":"2408.08670","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-route-for-dynamic-adapter","title":"Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models","date":"2024-08-16","arxiv_id":"2408.09053","repositories_listed":0,"syntology":null},{"url":null,"slug":"llmi3d-empowering-llm-with-3d-perception-from","title":"LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image","date":"2024-08-14","arxiv_id":"2408.07422","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-words-to-worth-newborn-article-impact","title":"From Words to Worth: Newborn Article Impact Prediction with LLM","date":"2024-08-07","arxiv_id":"2408.03934","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-03172","title":"Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi","date":"2024-08-06","arxiv_id":"2408.03172","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-03290","title":"SARA: Singular-Value Based Adaptive Low-Rank Adaption","date":"2024-08-06","arxiv_id":"2408.03290","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastedit-fast-text-guided-single-image","title":"FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning","date":"2024-08-06","arxiv_id":"2408.03355","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01008","title":"Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs","date":"2024-08-02","arxiv_id":"2408.01008","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01505","title":"MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts","date":"2024-08-02","arxiv_id":"2408.01505","repositories_listed":0,"syntology":null},{"url":null,"slug":"2407-21066","title":"ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks","date":"2024-07-28","arxiv_id":"2407.21066","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-via-circular","title":"Parameter-Efficient Fine-Tuning via Circular Convolution","date":"2024-07-27","arxiv_id":"2407.19342","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-for-continual","title":"Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective","date":"2024-07-24","arxiv_id":"2407.17120","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-embeddings-inform-learning-and","title":"Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders","date":"2024-07-22","arxiv_id":"2407.15731","repositories_listed":0,"syntology":null},{"url":null,"slug":"missing-modality-prediction-for-unpaired","title":"Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models","date":"2024-07-17","arxiv_id":"2407.12616","repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-generative-models-degenerate-the","title":"Turning Generative Models Degenerate: The Power of Data Poisoning Attacks","date":"2024-07-17","arxiv_id":"2407.12281","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-the-efficacy-of-federated-parameter","title":"Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification","date":"2024-07-16","arxiv_id":"2407.11573","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-for-multi","title":"Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction","date":"2024-07-10","arxiv_id":"2407.07517","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-vs-retro-exploring-the-intersection-of","title":"GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning","date":"2024-07-05","arxiv_id":"2407.04528","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-decoder-only-large-language","title":"Investigating Decoder-only Large Language Models for Speech-to-text Translation","date":"2024-07-03","arxiv_id":"2407.03169","repositories_listed":0,"syntology":null},{"url":null,"slug":"catmemo-at-the-finllm-challenge-task-fine","title":"CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications","date":"2024-07-02","arxiv_id":"2407.01953","repositories_listed":0,"syntology":null},{"url":null,"slug":"finecliper-multi-modal-fine-grained-clip-for","title":"FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs","date":"2024-07-02","arxiv_id":"2407.02157","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperloader-integrating-hypernetwork-based","title":"HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling","date":"2024-07-01","arxiv_id":"2407.01411","repositories_listed":0,"syntology":null},{"url":null,"slug":"splitlora-a-split-parameter-efficient-fine","title":"SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models","date":"2024-07-01","arxiv_id":"2407.00952","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-scalable-exact-machine-unlearning","title":"Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning","date":"2024-06-24","arxiv_id":"2406.16257","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-adversarial-learning-for-robust","title":"Federated Adversarial Learning for Robust Autonomous Landing Runway Detection","date":"2024-06-22","arxiv_id":"2406.15925","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-the-global-synergies-in-low-rank","title":"Unlocking the Global Synergies in Low-Rank Adapters","date":"2024-06-21","arxiv_id":"2406.14956","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-lora-lora-based-parameter-efficient","title":"Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates","date":"2024-06-18","arxiv_id":"2406.13046","repositories_listed":0,"syntology":null},{"url":null,"slug":"explora-parameter-efficient-extended-pre","title":"ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts","date":"2024-06-16","arxiv_id":"2406.10973","repositories_listed":0,"syntology":null},{"url":null,"slug":"promoting-data-and-model-privacy-in-federated","title":"Promoting Data and Model Privacy in Federated Learning through Quantized LoRA","date":"2024-06-16","arxiv_id":"2406.10976","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-active-learning-for","title":"Parameter-Efficient Active Learning for Foundational models","date":"2024-06-13","arxiv_id":"2406.09296","repositories_listed":0,"syntology":null},{"url":null,"slug":"pc-lora-low-rank-adaptation-for-progressive","title":"PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation","date":"2024-06-13","arxiv_id":"2406.09117","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-parameter-efficient-language-extension","title":"A Parameter-efficient Language Extension Framework for Multilingual ASR","date":"2024-06-10","arxiv_id":"2406.06329","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-backdoor-attacks-and-defenses-on","title":"A Survey of Recent Backdoor Attacks and Defenses in Large Language Models","date":"2024-06-10","arxiv_id":"2406.06852","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-empirical-fisher-approximation","title":"An Improved Empirical Fisher Approximation for Natural Gradient Descent","date":"2024-06-10","arxiv_id":"2406.06420","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-differentially-private-fine-tuning","title":"Efficient Differentially Private Fine-Tuning of Diffusion Models","date":"2024-06-07","arxiv_id":"2406.05257","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypernetworks-for-personalizing-asr-to","title":"Hypernetworks for Personalizing ASR to Atypical Speech","date":"2024-06-06","arxiv_id":"2406.04240","repositories_listed":0,"syntology":null},{"url":null,"slug":"vhdl-eval-a-framework-for-evaluating-large","title":"VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation","date":"2024-06-06","arxiv_id":"2406.04379","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapter-x-a-novel-general-parameter-efficient","title":"Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision","date":"2024-06-05","arxiv_id":"2406.03051","repositories_listed":0,"syntology":null},{"url":null,"slug":"choice-of-peft-technique-in-continual","title":"Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need","date":"2024-06-05","arxiv_id":"2406.03216","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-fine-tuning-of","title":"Differentially Private Fine-Tuning of Diffusion Models","date":"2024-06-03","arxiv_id":"2406.01355","repositories_listed":0,"syntology":null},{"url":null,"slug":"revolutionizing-large-language-model-training","title":"SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information","date":"2024-06-03","arxiv_id":"2406.06564","repositories_listed":0,"syntology":null},{"url":null,"slug":"mamba-state-space-models-are-lyapunov-stable","title":"Mamba State-Space Models Are Lyapunov-Stable Learners","date":"2024-05-31","arxiv_id":"2406.00209","repositories_listed":0,"syntology":null},{"url":"/paper/sam-e-leveraging-visual-foundation-model-with","slug":"sam-e-leveraging-visual-foundation-model-with","title":"SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation","date":"2024-05-30","arxiv_id":"2405.19586","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-in","title":"Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation","date":"2024-05-29","arxiv_id":"2405.18840","repositories_listed":0,"syntology":null},{"url":null,"slug":"rap-efficient-text-video-retrieval-with","title":"RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter","date":"2024-05-29","arxiv_id":"2405.19465","repositories_listed":0,"syntology":null},{"url":null,"slug":"iapt-instruction-aware-prompt-tuning-for","title":"IAPT: Instruction-Aware Prompt Tuning for Large Language Models","date":"2024-05-28","arxiv_id":"2405.18203","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-are-beacons-a-semantic-perspective","title":"Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning","date":"2024-05-28","arxiv_id":"2405.18292","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsity-and-hybridity-inspired-visual","title":"Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis","date":"2024-05-28","arxiv_id":"2405.17877","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-corrected-multimodal-large-language","title":"Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation","date":"2024-05-27","arxiv_id":"2405.17418","repositories_listed":0,"syntology":null},{"url":null,"slug":"textit-trans-lora-towards-data-free","title":"$\\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning","date":"2024-05-27","arxiv_id":"2405.17258","repositories_listed":0,"syntology":null},{"url":null,"slug":"bisup-bidirectional-quantization-error","title":"BiSup: Bidirectional Quantization Error Suppression for Large Language Models","date":"2024-05-24","arxiv_id":"2405.15346","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-trained-vision-language-models-as-partial","title":"Pre-Trained Vision-Language Models as Partial Annotators","date":"2024-05-23","arxiv_id":"2406.18550","repositories_listed":0,"syntology":null},{"url":null,"slug":"fett-continual-class-incremental-learning-via","title":"FeTT: Continual Class Incremental Learning via Feature Transformation Tuning","date":"2024-05-20","arxiv_id":"2405.11822","repositories_listed":0,"syntology":null},{"url":null,"slug":"haris-human-like-attention-for-reference","title":"HARIS: Human-Like Attention for Reference Image Segmentation","date":"2024-05-17","arxiv_id":"2405.10707","repositories_listed":0,"syntology":null},{"url":null,"slug":"sa-fedlora-adaptive-parameter-allocation-for","title":"SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning","date":"2024-05-15","arxiv_id":"2405.09394","repositories_listed":0,"syntology":null},{"url":null,"slug":"dp-dylora-fine-tuning-transformer-based","title":"DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation","date":"2024-05-10","arxiv_id":"2405.06368","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-with-adapters","title":"Parameter-Efficient Fine-Tuning With Adapters","date":"2024-05-09","arxiv_id":"2405.05493","repositories_listed":0,"syntology":null},{"url":null,"slug":"selective-fine-tuning-on-llm-labeled-data-may","title":"Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection","date":"2024-05-09","arxiv_id":"2405.06093","repositories_listed":0,"syntology":null},{"url":null,"slug":"coursegpt-zh-an-educational-large-language","title":"CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization","date":"2024-05-08","arxiv_id":"2405.04781","repositories_listed":0,"syntology":null},{"url":null,"slug":"elite-efficient-image-to-lidar-knowledge","title":"ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation","date":"2024-05-07","arxiv_id":"2405.04121","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-news-summarization-with-elearnfit","title":"Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning","date":"2024-05-04","arxiv_id":"2405.02710","repositories_listed":0,"syntology":null},{"url":null,"slug":"tartunlp-at-evalatin-2024-emotion-polarity","title":"TartuNLP at EvaLatin 2024: Emotion Polarity Detection","date":"2024-05-02","arxiv_id":"2405.01159","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-automatic-scoring-and-feedback","title":"Investigating Automatic Scoring and Feedback using Large Language Models","date":"2024-05-01","arxiv_id":"2405.00602","repositories_listed":0,"syntology":null},{"url":null,"slug":"mopeft-a-mixture-of-pefts-for-the-segment","title":"MoPEFT: A Mixture-of-PEFTs for the Segment Anything Model","date":"2024-05-01","arxiv_id":"2405.00293","repositories_listed":0,"syntology":null},{"url":"/paper/rst-lora-a-discourse-aware-low-rank","slug":"rst-lora-a-discourse-aware-low-rank","title":"RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization","date":"2024-05-01","arxiv_id":"2405.00657","repositories_listed":0,"syntology":{"n":7,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rst-lora-a-discourse-aware-low-rank#ran","syntology_url":"https://syntology.ai/paper/2405.00657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.00657"}},"official":null}},{"url":null,"slug":"spafit-stratified-progressive-adaptation-fine","title":"SPAFIT: Stratified Progressive Adaptation Fine-tuning for Pre-trained Large Language Models","date":"2024-04-30","arxiv_id":"2405.00201","repositories_listed":0,"syntology":null},{"url":null,"slug":"federa-efficient-fine-tuning-of-language","title":"FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition","date":"2024-04-29","arxiv_id":"2404.18848","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-tuning-large-language","title":"Parameter-Efficient Tuning Large Language Models for Graph Representation Learning","date":"2024-04-28","arxiv_id":"2404.18271","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiency-in-focus-layernorm-as-a-catalyst","title":"Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models","date":"2024-04-25","arxiv_id":"2404.16385","repositories_listed":0,"syntology":null},{"url":null,"slug":"gated-low-rank-adaptation-for-personalized","title":"Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices","date":"2024-04-24","arxiv_id":"2406.02562","repositories_listed":0,"syntology":null},{"url":null,"slug":"external-prompt-features-enhanced-parameter","title":"External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection","date":"2024-04-23","arxiv_id":"2404.15008","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-efficient-fine-tuning-a","title":"Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications","date":"2024-04-21","arxiv_id":"2404.13506","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlora-trocr-mixed-text-mode-optical-character","title":"Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer","date":"2024-04-19","arxiv_id":"2404.12734","repositories_listed":0,"syntology":null},{"url":null,"slug":"itbls-a-dataset-of-interactive-conversations","title":"iTBLS: A Dataset of Interactive Conversations Over Tabular Information","date":"2024-04-19","arxiv_id":"2404.12580","repositories_listed":0,"syntology":null},{"url":null,"slug":"tartunlp-sigtyp-2024-shared-task-adapting-xlm","title":"TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages","date":"2024-04-19","arxiv_id":"2404.12845","repositories_listed":0,"syntology":null},{"url":null,"slug":"skip-skill-localized-prompt-tuning-for","title":"Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning","date":"2024-04-18","arxiv_id":"2404.11916","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-and-efficient-unlearning-for-large","title":"Exact and Efficient Unlearning for Large Language Model-based Recommendation","date":"2024-04-16","arxiv_id":"2404.10327","repositories_listed":0,"syntology":null},{"url":null,"slug":"lora-dropout-as-a-sparsity-regularizer-for","title":"LoRA Dropout as a Sparsity Regularizer for Overfitting Control","date":"2024-04-15","arxiv_id":"2404.09610","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-federated-pipeline-for-parameter","title":"Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models","date":"2024-04-09","arxiv_id":"2404.06448","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-few-shot-learning-to-classify-primary","title":"Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures","date":"2024-04-09","arxiv_id":"2404.06080","repositories_listed":0,"syntology":null}],"record_sha256":"27020bb7bd16efd0c93ec2ecac7a7781d7cee4b5e699f6a2a04c6a86fed37bdd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}