arXiv AI
1d ago
Automating the Detection of Requirement Dependencies Using Large Language Models
arXiv:2602. 22456v2 Announce Type: replace-cross Abstract: Requirements are inherently interconnected through various types of dependencies.
Transfer learning for conflict and duplicate detection in software requirement pairs
arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.
Related stories
arXiv AI
Jul 3
Introduction to Transformers: an NLP Perspective
arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.
arXiv AI
Aug 6
A Unified Model for Cross-Domain Clone Detection via Model Merging
arXiv:2608. 04215v1 Announce Type: cross Abstract: The growing diversity of code clone types, from syntactic copies to cross-language semantic clones to AI-generated duplicates, has created a fragmentation crisis in clone detection.
arXiv AI
Jun 18
Towards Understanding What State Space Models Learn About Code
arXiv:2602. 06774v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as an efficient alternative to the Transformer architecture.
arXiv AI
Jun 9
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.
arXiv Machine Learning
Jul 21
Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks
arXiv:2607. 17624v1 Announce Type: new Abstract: Transformers are remarkably versatile and their design is largely consistent across a variety of applications.
arXiv AI
23h ago
Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation
arXiv:2603. 23047v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open whether these gains transfer to specialized tasks.
arXiv AI
Aug 11
One Adapter Pair per Model: A Universal Activation Interface for Language Models
arXiv:2608. 09521v1 Announce Type: new Abstract: Activation-based tools are usually tied to one model's native hidden space, requiring probes, sparse autoencoders, and natural-language interpreters to be rebuilt or rediscovered for each new language model.
arXiv AI
Jun 24
Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models
arXiv:2606. 24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing.
arXiv AI
Jul 14
SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.
arXiv AI
Jul 2
The State-Prediction Separation Hypothesis
arXiv:2607. 01218v1 Announce Type: cross Abstract: Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions.
arXiv AI
Jun 18
From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging
arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.