10 papers
TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
Amirreza Esmaeili, Fatemeh Fard
Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tool…
Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs
Fahd Seddik, Fatemeh Fard
We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal represe…
Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection
Mohamad Khajezade, Fatemeh H. Fard, Mohamed Sami Shehata
Cross-language code clone detection (X-CCD) is challenging because semantically equivalent programs written in different languages often share little surface similarity. Although l…
Context-Augmented Code Generation Using Programming Knowledge Graphs
Shahd Seddik, Fahd Seddik, Iman Saberi +3
Large Language Models (LLMs) excel at code generation but struggle with complex problems. Retrieval-Augmented Generation (RAG) mitigates this issue by integrating external knowledg…
FISCAL: Financial Synthetic Claim-document Augmented Learning for Efficient Fact-Checking
Rishab Sharma, Iman Saberi, Elham Alipour +2
Financial applications of large language models (LLMs) require factual reliability and computational efficiency, yet current systems often hallucinate details and depend on prohibi…
Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models
Amirreza Esmaeili, Fahd Seddik, Yongyi Ji +2
Programming languages can benefit from one another by utilizing a language model for software engineering tasks. Full fine-tuning and Parameter Efficient Fine-Tuning (PEFT) of Code…