On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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9 results for “instruction-finetuned”

Multimodal Web Navigation with Instruction-Finetuned Foundation Modelscs.LG

WebGUM learns web navigation from multimodal data, outperforming previous methods.

problem Limited generalization from domain-specific models in web navigation.

method Instruction-following multimodal agent trained on vision-language foundation models.

result Significant improvement in web navigation performance on benchmarks.

web navigation multimodal learning foundation models instruction-following vision-language

2023-05-19abs ↗pdf ↗

INFUSER: Influence-Guided Self-Evolution Improves Reasoningcs.LG

INFUSER improves reasoning by self-evolving with a generator and solver that co-learn from unstructured documents.

problem Improving reasoning through self-evolution

method INFUSER uses a generator and solver co-evolving in a document pool to improve reasoning.

result INFUSER outperforms strong self-evolution baselines on Olympiad and SuperGPQA benchmarks.

self-evolution reasoning co-training document pool instruction-finetuned

2026-06-08abs ↗pdf ↗

In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Modelscs.LG

In2Core selects a coreset for efficient LLM fine-tuning with reduced data.

problem Costly fine-tuning of large language models due to extensive parameters and data requirements.

method Analyzes model gradients to estimate training sample influence, optimizing for efficiency.

result Achieves similar performance with 50% of training data using In2Core.

Influence functions coresets instruction fine-tuning large language models model efficiency

2024-08-07abs ↗pdf ↗

Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognitionq-fin.CP

Paper fine-tunes LLaMA-3-8B for financial NER using instruction and LoRA.

problem LLMs struggle with financial NER, especially differentiating entities and amounts.

method Instruction fine-tuning combined with LoRA for parameter-efficient learning.

result Micro-F1 score of 0.894 on financial NER tasks, outperforming other models.

financial NER LLaMA-3-8B instruction fine-tuning LoRA state-of-the-art

2026-01-15abs ↗pdf ↗

Understanding and Improving Noisy Embedding Techniques in Instruction Finetuningcs.LG

SymNoise improves language model fine-tuning by 6.7% over NEFTune, using symmetric noise.

problem Improving performance of language models through noise-based fine-tuning.

method Introducing SymNoise, a new fine-tuning method using symmetric noise in embeddings.

result SymNoise increases model performance by 69.04% on AlpacaEval compared to NEFTune's 64.69%.

instruction fine-tuning language models symmetric noise embedding performance improvement

2026-05-22abs ↗pdf ↗

Financial Text Classification Based On rLoRA Finetuning On Qwen3-8B modelcs.LG

Qwen3-8B outperforms classical models in financial text classification.

problem Financial text classification for trading systems and sentiment analysis.

method Noisy Embedding Instruction Finetuning and Rank-stabilized Low-Rank Adaptation.

result Qwen3-8B achieves better classification accuracy and fewer training epochs.

financial text classification Qwen3-8B Noisy Embedding instruction-following low-rank optimization

2025-11-29abs ↗pdf ↗

INFUSER: Influence-Guided Self-Evolution Improves Reasoningcs.LG

INFUSER improves reasoning by co-evolving a generator and solver with adaptive curriculum.

problem Improving reasoning in language models with minimal external supervision.

method INFUSER uses a Generator and Solver to co-evolve iteratively, rewarding the Generator with an influence score and the Solver with correctness rewards.

result INFUSER outperforms self-evolution baselines by over 20% on Olympiad and SuperGPQA benchmarks.

self-evolution language models reasoning influence score co-training

2026-06-08abs ↗pdf ↗

Conformal Information Pursuit for Interactively Guiding Large Language Modelscs.LG

C-IP improves LLMs' query selection for interactive tasks by estimating uncertainty robustly.

problem Minimizing the number of queries for interactive LLMs.

method Conformal Information Pursuit (C-IP) using conformal prediction sets.

result C-IP achieves better predictive performance and shorter query-answer chains.

Large Language Models Interactive Queries Conformal Prediction Information Gain Uncertainty Estimation

2025-07-04abs ↗pdf ↗

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimizationstat.ML

Bayesian Optimization improves data mixture selection for large language models.

problem Optimizing the training data mixtures for large language models.

method Viewed as a black-box hyperparameter optimization problem, using Bayesian Optimization.

result Consistently strong results with speed-ups of over 500%.

Bayesian Optimization Data Mixture Language Models Hyperparameter Optimization Training Efficiency

2025-08-15abs ↗pdf ↗