{"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/question-answering/papers/49","list_of":"/task/question-answering","task":"Question Answering","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":49,"pages_in_order":109,"rows_per_page":100,"rows":[4801,4900],"of":10817,"counts":{"archive_papers_tagged":10817,"with_a_code_link":4171,"where_syntology_ran_a_sample":1274,"not_listed_spam_title":0,"listed":10817,"listed_where_code_ran":1274,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1073,"every_run_a_failure_of_syntologys_instrument":201,"listed_with_a_run_with_no_instrument_failure":1073,"listed_every_run_a_failure_of_syntologys_instrument":201,"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/question-answering","prev":"/task/question-answering/papers/48","next":"/task/question-answering/papers/50","papers":[{"url":null,"slug":"memory-efficient-fine-tuning-of-transformers","title":"Memory-Efficient Fine-Tuning of Transformers via Token Selection","date":"2025-01-31","arxiv_id":"2501.18824","repositories_listed":0,"syntology":null},{"url":null,"slug":"calm-unleashing-the-cross-lingual-self","title":"CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering","date":"2025-01-30","arxiv_id":"2501.18457","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-embedding-vs-task-specific-embedding","title":"General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance","date":"2025-01-30","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomy-might-be-all-you-need-forecasting","title":"Anatomy Might Be All You Need: Forecasting What to Do During Surgery","date":"2025-01-29","arxiv_id":"2501.18011","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-language-approach-for-quranic-qa","title":"Cross-Language Approach for Quranic QA","date":"2025-01-29","arxiv_id":"2501.17449","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-graphs-for-table-and-text-based","title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","date":"2025-01-29","arxiv_id":"2501.17767","repositories_listed":0,"syntology":null},{"url":null,"slug":"innerthoughts-disentangling-representations","title":"InnerThoughts: Disentangling Representations and Predictions in Large Language Models","date":"2025-01-29","arxiv_id":"2501.17994","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-moe-a-mixture-of-experts-multi-modal-llm","title":"3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow","date":"2025-01-28","arxiv_id":"2501.16698","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-evaluation-for-a-large-scale","title":"Comprehensive Evaluation for a Large Scale Knowledge Graph Question Answering Service","date":"2025-01-28","arxiv_id":"2501.17270","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-abstraction-level-retrieve-augment","title":"Multiple Abstraction Level Retrieve Augment Generation","date":"2025-01-28","arxiv_id":"2501.16952","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-study-on-fine-tuning-large","title":"A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis","date":"2025-01-27","arxiv_id":"2501.17190","repositories_listed":0,"syntology":null},{"url":null,"slug":"options-aware-dense-retrieval-for-multiple","title":"Options-Aware Dense Retrieval for Multiple-Choice query Answering","date":"2025-01-27","arxiv_id":"2501.16111","repositories_listed":0,"syntology":null},{"url":null,"slug":"pisco-pretty-simple-compression-for-retrieval","title":"PISCO: Pretty Simple Compression for Retrieval-Augmented Generation","date":"2025-01-27","arxiv_id":"2501.16075","repositories_listed":0,"syntology":null},{"url":null,"slug":"provence-efficient-and-robust-context-pruning","title":"Provence: efficient and robust context pruning for retrieval-augmented generation","date":"2025-01-27","arxiv_id":"2501.16214","repositories_listed":0,"syntology":null},{"url":null,"slug":"urag-implementing-a-unified-hybrid-rag-for","title":"URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT","date":"2025-01-27","arxiv_id":"2501.16276","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-biomedical-abstracts-into-plain","title":"Adapting Biomedical Abstracts into Plain language using Large Language Models","date":"2025-01-26","arxiv_id":"2501.15700","repositories_listed":0,"syntology":null},{"url":null,"slug":"complete-chess-games-enable-llm-become-a","title":"Complete Chess Games Enable LLM Become A Chess Master","date":"2025-01-26","arxiv_id":"2501.17186","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-mitigate-information-loss-in-knowledge","title":"How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback","date":"2025-01-26","arxiv_id":"2501.15378","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-large-vision-language-models-for","title":"Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis","date":"2025-01-26","arxiv_id":"2501.15370","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attempt-to-unraveling-token-prediction","title":"An Attempt to Unraveling Token Prediction Refinement and Identifying Essential Layers of Large Language Models","date":"2025-01-25","arxiv_id":"2501.15054","repositories_listed":0,"syntology":null},{"url":null,"slug":"asrank-zero-shot-re-ranking-with-answer-scent","title":"ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval","date":"2025-01-25","arxiv_id":"2501.15245","repositories_listed":0,"syntology":null},{"url":null,"slug":"cg-rag-research-question-answering-by","title":"CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs","date":"2025-01-25","arxiv_id":"2501.15067","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-retrieval-augmented-generation-for","title":"Federated Retrieval Augmented Generation for Multi-Product Question Answering","date":"2025-01-25","arxiv_id":"2501.14998","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-hierarchy-guided-biological-medical","title":"Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training","date":"2025-01-25","arxiv_id":"2501.15108","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-evaluation-based-on-aerospace","title":"LLM Evaluation Based on Aerospace Manufacturing Expertise: Automated Generation and Multi-Model Question Answering","date":"2025-01-25","arxiv_id":"2501.17183","repositories_listed":0,"syntology":null},{"url":null,"slug":"chain-of-retrieval-augmented-generation","title":"Chain-of-Retrieval Augmented Generation","date":"2025-01-24","arxiv_id":"2501.14342","repositories_listed":0,"syntology":null},{"url":"/paper/enter-event-based-interpretable-reasoning-for","slug":"enter-event-based-interpretable-reasoning-for","title":"ENTER: Event Based Interpretable Reasoning for VideoQA","date":"2025-01-24","arxiv_id":"2501.14194","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphbc-improving-llms-for-better-graph-data","title":"GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better","date":"2025-01-24","arxiv_id":"2501.14427","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretability-analysis-of-domain-adapted","title":"Interpretability Analysis of Domain Adapted Dense Retrievers","date":"2025-01-24","arxiv_id":"2501.14459","repositories_listed":0,"syntology":null},{"url":null,"slug":"master-a-multi-agent-system-with-llm","title":"MASTER: A Multi-Agent System with LLM Specialized MCTS","date":"2025-01-24","arxiv_id":"2501.14304","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-understanding-enabled-semantic","title":"Scene Understanding Enabled Semantic Communication with Open Channel Coding","date":"2025-01-24","arxiv_id":"2501.14520","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-better-understanding-table","title":"Towards Better Understanding Table Instruction Tuning: Decoupling the Effects from Data versus Models","date":"2025-01-24","arxiv_id":"2501.14717","repositories_listed":0,"syntology":null},{"url":null,"slug":"verify-with-caution-the-pitfalls-of-relying","title":"Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics","date":"2025-01-24","arxiv_id":"2501.14883","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-modeling-and-question-answering","title":"Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs","date":"2025-01-23","arxiv_id":"2501.13984","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-contextual-faithfulness-of-large","title":"Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization","date":"2025-01-23","arxiv_id":"2501.13573","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-on-patient-medical-records","title":"Question Answering on Patient Medical Records with Private Fine-Tuned LLMs","date":"2025-01-23","arxiv_id":"2501.13687","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasvqa-advancing-videoqa-with-imperfect","title":"ReasVQA: Advancing VideoQA with Imperfect Reasoning Process","date":"2025-01-23","arxiv_id":"2501.13536","repositories_listed":0,"syntology":null},{"url":"/paper/adaptive-retrieval-without-self-knowledge","slug":"adaptive-retrieval-without-self-knowledge","title":"Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home","date":"2025-01-22","arxiv_id":"2501.12835","repositories_listed":0,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-retrieval-without-self-knowledge#ran","syntology_url":"https://syntology.ai/paper/2501.12835","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.12835"}},"official":null}},{"url":null,"slug":"combining-knowledge-graph-and-llms-for","title":"Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering","date":"2025-01-22","arxiv_id":"2501.12697","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidencemap-unleashing-the-power-of-small","title":"EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering","date":"2025-01-22","arxiv_id":"2501.12746","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirically-grounded-tool-for-automatic","title":"An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts","date":"2025-01-21","arxiv_id":"2501.12521","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-xllms-understand-the-structure-of-dialog","title":"Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios","date":"2025-01-20","arxiv_id":"2501.11269","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-policy-de-composition-in","title":"Few-shot Policy (de)composition in Conversational Question Answering","date":"2025-01-20","arxiv_id":"2501.11335","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-to-question-retrieval-for","title":"Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering","date":"2025-01-20","arxiv_id":"2501.11301","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dual-use-dilemma-in-llms-do-empowering","title":"The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility?","date":"2025-01-20","arxiv_id":"2501.13952","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-collection-of-question-answering-datasets","title":"A Collection of Question Answering Datasets for Norwegian","date":"2025-01-19","arxiv_id":"2501.11128","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-chain-of-thought-towards","title":"Leveraging Chain of Thought towards Empathetic Spoken Dialogue without Corresponding Question-Answering Data","date":"2025-01-19","arxiv_id":"2501.10937","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-method-for-multi-hop-question-answering-on","title":"A Method for Multi-Hop Question Answering on Persian Knowledge Graph","date":"2025-01-18","arxiv_id":"2501.16350","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-multimodal-llms-do-visual-temporal","title":"Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!","date":"2025-01-18","arxiv_id":"2501.10674","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-imbalanced-aware-adaptive-dataset","title":"Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring","date":"2025-01-18","arxiv_id":"2501.10677","repositories_listed":0,"syntology":null},{"url":null,"slug":"passage-segmentation-of-documents-for","title":"Passage Segmentation of Documents for Extractive Question Answering","date":"2025-01-17","arxiv_id":"2501.09940","repositories_listed":0,"syntology":null},{"url":null,"slug":"tabular-tx-theme-explanation-structure-based","title":"Tabular-TX: Theme-Explanation Structure-based Table Summarization via In-Context Learning","date":"2025-01-17","arxiv_id":"2501.10487","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithm-for-semantic-network-generation","title":"Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili","date":"2025-01-16","arxiv_id":"2501.09326","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-a-large-language-model-with-a","title":"Augmenting a Large Language Model with a Combination of Text and Visual Data for Conversational Visualization of Global Geospatial Data","date":"2025-01-16","arxiv_id":"2501.09521","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-text-extraction-with-large","title":"Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems","date":"2025-01-16","arxiv_id":"2501.09801","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-visual-defense-adversarial-pre","title":"Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness","date":"2025-01-16","arxiv_id":"2501.09446","repositories_listed":0,"syntology":null},{"url":null,"slug":"perspective-transition-of-large-language","title":"Perspective Transition of Large Language Models for Solving Subjective Tasks","date":"2025-01-16","arxiv_id":"2501.09265","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-retrieve-or-not-to-retrieve-uncertainty","title":"To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation","date":"2025-01-16","arxiv_id":"2501.09292","repositories_listed":0,"syntology":null},{"url":null,"slug":"admitting-ignorance-helps-the-video-question","title":"Admitting Ignorance Helps the Video Question Answering Models to Answer","date":"2025-01-15","arxiv_id":"2501.08771","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-knowledge-integration-for-enhanced","title":"Dynamic Knowledge Integration for Enhanced Vision-Language Reasoning","date":"2025-01-15","arxiv_id":"2501.08597","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-scene-understanding-for-vision","title":"Embodied Scene Understanding for Vision Language Models via MetaVQA","date":"2025-01-15","arxiv_id":"2501.09167","repositories_listed":0,"syntology":null},{"url":null,"slug":"stella-a-structured-grading-system-using-llms","title":"SteLLA: A Structured Grading System Using LLMs with RAG","date":"2025-01-15","arxiv_id":"2501.09092","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multilingual-llm-evaluation-for","title":"Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A study on Lithuanian History","date":"2025-01-15","arxiv_id":"2501.09154","repositories_listed":0,"syntology":null},{"url":null,"slug":"advice-for-diabetes-self-management-by","title":"Advice for Diabetes Self-Management by ChatGPT Models: Challenges and Recommendations","date":"2025-01-14","arxiv_id":"2501.07931","repositories_listed":0,"syntology":null},{"url":null,"slug":"astrid-an-automated-and-scalable-triad-for","title":"ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems","date":"2025-01-14","arxiv_id":"2501.08208","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-energy-efficiency-and","title":"Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings","date":"2025-01-14","arxiv_id":"2501.08219","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-with-graphs-structuring-implicit","title":"Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning","date":"2025-01-14","arxiv_id":"2501.07845","repositories_listed":0,"syntology":null},{"url":null,"slug":"sar-strikes-back-a-new-hope-for-rsvqa","title":"SAR Strikes Back: A New Hope for RSVQA","date":"2025-01-14","arxiv_id":"2501.08131","repositories_listed":0,"syntology":null},{"url":null,"slug":"talk-to-right-specialists-routing-and","title":"Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering","date":"2025-01-14","arxiv_id":"2501.07813","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-key-value-cache-fusion-for-position","title":"Parallel Key-Value Cache Fusion for Position Invariant RAG","date":"2025-01-13","arxiv_id":"2501.07523","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-quest-for-visual-understanding-a-journey","title":"The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering","date":"2025-01-13","arxiv_id":"2501.07109","repositories_listed":0,"syntology":null},{"url":null,"slug":"timelogic-a-temporal-logic-benchmark-for","title":"TimeLogic: A Temporal Logic Benchmark for Video QA","date":"2025-01-13","arxiv_id":"2501.07214","repositories_listed":0,"syntology":null},{"url":null,"slug":"geopix-multi-modal-large-language-model-for","title":"GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing","date":"2025-01-12","arxiv_id":"2501.06828","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-token-probability-guided-rag-for","title":"First Token Probability Guided RAG for Telecom Question Answering","date":"2025-01-11","arxiv_id":"2501.06468","repositories_listed":0,"syntology":null},{"url":null,"slug":"bactrainus-optimizing-large-language-models","title":"Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks","date":"2025-01-10","arxiv_id":"2501.06286","repositories_listed":0,"syntology":null},{"url":null,"slug":"finnish-squad-a-simple-approach-to-machine","title":"Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations","date":"2025-01-10","arxiv_id":"2501.05963","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-language-priors-for-visual","title":"Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation","date":"2025-01-10","arxiv_id":"2501.05690","repositories_listed":0,"syntology":null},{"url":"/paper/peace-empowering-geologic-map-holistic","slug":"peace-empowering-geologic-map-holistic","title":"PEACE: Empowering Geologic Map Holistic Understanding with MLLMs","date":"2025-01-10","arxiv_id":"2501.06184","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/peace-empowering-geologic-map-holistic#ran","syntology_url":"https://syntology.ai/paper/2501.06184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.06184"}},"official":null}},{"url":null,"slug":"commonsense-video-question-answering-through","title":"Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning","date":"2025-01-09","arxiv_id":"2501.05069","repositories_listed":0,"syntology":null},{"url":null,"slug":"llava-octopus-unlocking-instruction-driven","title":"LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding","date":"2025-01-09","arxiv_id":"2501.05067","repositories_listed":0,"syntology":null},{"url":null,"slug":"sugar-leveraging-contextual-confidence-for","title":"SUGAR: Leveraging Contextual Confidence for Smarter Retrieval","date":"2025-01-09","arxiv_id":"2501.04899","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-retrieval-based-on-generative-ai","title":"Knowledge Retrieval Based on Generative AI","date":"2025-01-08","arxiv_id":"2501.04635","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-uncertainty-quantification-for","title":"Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks","date":"2025-01-08","arxiv_id":"2501.04234","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervision-free-vision-language-alignment","title":"Feedback-Driven Vision-Language Alignment with Minimal Human Supervision","date":"2025-01-08","arxiv_id":"2501.04568","repositories_listed":0,"syntology":null},{"url":"/paper/kanoclip-zero-shot-anomaly-detection-through","slug":"kanoclip-zero-shot-anomaly-detection-through","title":"KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration","date":"2025-01-07","arxiv_id":"2501.03786","repositories_listed":0,"syntology":null},{"url":null,"slug":"localizing-ai-evaluating-open-weight-language","title":"Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States","date":"2025-01-07","arxiv_id":"2501.03952","repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-open-qa-on-the-mia-shared-task","title":"Multilingual Open QA on the MIA Shared Task","date":"2025-01-07","arxiv_id":"2501.04153","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-multihop-source-retrieval-for-web","title":"Multimodal Multihop Source Retrieval for Web Question Answering","date":"2025-01-07","arxiv_id":"2501.04173","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-question-answering-from-early","title":"Visual question answering: from early developments to recent advances -- a survey","date":"2025-01-07","arxiv_id":"2501.03939","repositories_listed":0,"syntology":null},{"url":null,"slug":"boundingdocs-a-unified-dataset-for-document","title":"BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations","date":"2025-01-06","arxiv_id":"2501.03403","repositories_listed":0,"syntology":null},{"url":null,"slug":"flipedrag-black-box-opinion-manipulation","title":"FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models","date":"2025-01-06","arxiv_id":"2501.02968","repositories_listed":0,"syntology":null},{"url":null,"slug":"quim-rag-advancing-retrieval-augmented","title":"QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance","date":"2025-01-06","arxiv_id":"2501.02702","repositories_listed":0,"syntology":null},{"url":null,"slug":"accounting-for-focus-ambiguity-in-visual","title":"Accounting for Focus Ambiguity in Visual Questions","date":"2025-01-04","arxiv_id":"2501.02201","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-on-question-answering-over-visually","title":"Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends","date":"2025-01-04","arxiv_id":"2501.02235","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-large-language-models-with-some","title":"A Survey on Large Language Models with some Insights on their Capabilities and Limitations","date":"2025-01-03","arxiv_id":"2501.04040","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-face-anti-spoofing-enhancing","title":"Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models","date":"2025-01-03","arxiv_id":"2501.01720","repositories_listed":0,"syntology":null},{"url":null,"slug":"mocoll-agent-based-specific-and-general-model","title":"MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning","date":"2025-01-03","arxiv_id":"2501.01834","repositories_listed":0,"syntology":null},{"url":null,"slug":"quarch-a-question-answering-dataset-for-ai","title":"QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture","date":"2025-01-03","arxiv_id":"2501.01892","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-essence-of-contextual-understanding-in","title":"The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters","date":"2025-01-03","arxiv_id":"2501.01705","repositories_listed":0,"syntology":null},{"url":null,"slug":"citations-and-trust-in-llm-generated","title":"Citations and Trust in LLM Generated Responses","date":"2025-01-02","arxiv_id":"2501.01303","repositories_listed":0,"syntology":null}],"record_sha256":"e17df098ad15b7a5c9aaf8c54a9478273905d53ed217cb225c2904d2fa05467b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}