Tootfinder

Leveraging Large Language Model for Information Retrieval-based Bug Localization
Moumita Asad, Rafed Muhammad Yasir, Armin Geramirad, Sam Malek
https://arxiv.org/abs/2508.00253 …

Leveraging Large Language Model for Information Retrieval-based Bug Localization
Information Retrieval-based Bug Localization aims to identify buggy source files for a given bug report. While existing approaches -- ranging from vector space models to deep learning models -- have shown potential in this domain, their effectiveness is often limited by the vocabulary mismatch between bug reports and source code. To address this issue, we propose a novel Large Language Model (LLM) based bug localization approach, called GenLoc. Given a bug report, GenLoc leverages an LLM equipp…

Beyond the Wavefunction: Qualia Abstraction Language Mechanics and the Grammar of Awareness
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2508.02755 https://

Beyond the Wavefunction: Qualia Abstraction Language Mechanics and the Grammar of Awareness
We propose a formal reconstruction of quantum mechanics grounded not in external mathematical abstractions, but in the structured dynamics of subjective experience. The Qualia Abstraction Language (QAL) models physical systems as evolving streams of introspective units, structured sequences of modality, shape, and functional effect, rather than as state vectors in Hilbert space. This approach reimagines core quantum concepts: superposition becomes a form of structured ambiguity; collapse is ref…

This https://arxiv.org/abs/2502.18470 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Spatial Reasoning Questions
Spatial reasoning remains a challenge for Large Language Models (LLMs), which struggle with spatial data retrieval and reasoning. We propose Spatial Retrieval-Augmented Generation (Spatial-RAG), a framework that extends RAG to spatial tasks by integrating sparse spatial retrieval (spatial databases) and dense semantic retrieval (LLM-based similarity). A multi-objective ranking strategy balances spatial constraints and semantic relevance, while an LLM-guided generator ensures coherent responses.…

This https://arxiv.org/abs/2412.18407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Statistical Framework for Ranking LLM-Based Chatbots
Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended conversational tasks. Building upon this foundation, we propose a statistical framework that incorporates key advancements to address specif…

This https://arxiv.org/abs/2503.16456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
This position paper argues for a fundamental shift in how Large Language Models (LLMs) are integrated into the mental health care domain. We advocate for their role as co-creators rather than mere assistive tools. While LLMs have the potential to enhance accessibility, personalization, and crisis intervention, their adoption remains limited due to concerns about bias, evaluation, over-reliance, dehumanization, and regulatory uncertainties. To address these challenges, we propose two structured …

aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe, Ryo Tamura, Koji Tsuda
https://arxiv.org/abs/2507.22558 https://…

aLLoyM: A large language model for alloy phase diagram prediction
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagr…

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
Hosein Azarbonyad, Zi Long Zhu, Georgios Cheirmpos, Zubair Afzal, Vikrant Yadav, Georgios Tsatsaronis
https://arxiv.org/abs/2507.13827

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
When deciding to read an article or incorporate it into their research, scholars often seek to quickly identify and understand its main ideas. In this paper, we aim to extract these key concepts and contributions from scientific articles in the form of Question and Answer (QA) pairs. We propose two distinct approaches for generating QAs. The first approach involves selecting salient paragraphs, using a Large Language Model (LLM) to generate questions, ranking these questions by the likelihood o…

Shrinking the Generation-Verification Gap with Weak Verifiers
Jon Saad-Falcon, E. Kelly Buchanan, Mayee F. Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott Linderman, Azalia Mirhoseini, Christopher R\'e
https://arxiv.org/abs/2506.18203

Shrinking the Generation-Verification Gap with Weak Verifiers
Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates. Currently, high-quality verifiers are either unscalable (e.g., humans) or limited in utility (e.g., tools like Lean). While LM judges and reward models have become broadly useful as general-purpose verifiers, a significant performance gap remains between them and oracle verifiers (verifiers with perfect accuracy). To help close this gap, we introduce Weaver, a framework for designing a …

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
Pascal J. Sager, Ashwini Kamaraj, Benjamin F. Grewe, Thilo Stadelmann
https://arxiv.org/abs/2505.23250

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
We present the methodology and results of the Deep Retrieval team for subtask 4b of the CLEF CheckThat! 2025 competition, which focuses on retrieving relevant scientific literature for given social media posts. To address this task, we propose a hybrid retrieval pipeline that combines lexical precision, semantic generalization, and deep contextual re-ranking, enabling robust retrieval that bridges the informal-to-formal language gap. Specifically, we combine BM25-based keyword matching with a F…

This https://arxiv.org/abs/2502.17057 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the retriever as separate modules, overlooking the alignment between generation and ranking objectives. In this work, we propose ExpandR, a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. ExpandR employs the LLM to generate semantically rich query expansions, which are…