Natural language processing

Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.

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arXiv AI

Jul 29

TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation

arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.

By Ziyu Zheng, Zhengshun Du, Yaming Yang, Bin Tong, Guan Wang, Meng Yan, Ziyu Guan, Wei Zhao

rag nlp benchmarks

arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

llms rag nlp benchmarks

arXiv AI

Jul 29

When Thinking Before Retrieval Hurts: TraceBound Diagnostics for Adaptive Knowledge-Graph Retrieval

arXiv:2607. 24800v1 Announce Type: cross Abstract: Adaptive retrieval promises to make knowledge-graph question answering more robust by letting a controller search, inspect neighborhoods, revise actions, and stop when evidence is sufficient.

By Partha Sarathi Purkayastha (ETH Z\"urich)

nlp

arXiv AI

Jul 29

Automatic Knowledge Graph Construction and Query for Earthquake Catalogs

arXiv:2607. 24984v1 Announce Type: cross Abstract: In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence?

By Yuxin Zhou, Huai Zhang, S. Mostafa Mousavi

llms rag nlp

arXiv AI

Jul 29

AI-Assisted Knowledge Access for Legacy Enterprise Asset Management in Energy Operations: A Practical Retrieval System

arXiv:2607. 24792v1 Announce Type: cross Abstract: Energy utilities still run engineering work management, engineering procurement, and inventory processes on long-lived enterprise asset management platforms.

By Dave Mercier, Mishca de Costa, Muhammad Anwar, Mark Randall, Issam Hammad

nlp

arXiv AI

Jul 29

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance

arXiv:2607. 24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.

By Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad

llms rag agents nlp fine-tuning

arXiv AI

Jul 29

Salient Knowledge Pathways: Sparse Cross-Modal Routing for Efficient Knowledge-Intensive Multimodal Question Answering

arXiv:2607. 25422v1 Announce Type: new Abstract: Knowledge-intensive multimodal question answering (KI-MMQA) sits at the intersection of three expensive primitives: long visual token sequences, dense retrieval over large external corpora, and full cross-modal fusion.

By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

nlp efficiency multimodal benchmarks

arXiv AI

Jul 29

VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents

arXiv:2607. 24748v1 Announce Type: cross Abstract: Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables, charts, and figures.

By Seonok Kim

rag agents nlp multimodal benchmarks

arXiv AI

Jul 29

ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop

arXiv:2607. 24770v1 Announce Type: new Abstract: Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task progress, reason about spatial state, and recover from errors while performing physical actions.

By Azizul Zahid, Subrata Biswas, Bashima Islam, Sai Swaminathan

llms agents nlp multimodal safety

arXiv AI

Jul 29

MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

arXiv:2607. 24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs).

By Seongwon Seo, Seung Hwan Cho, Young-Min Kim

llms rag nlp fine-tuning benchmarks

arXiv AI

Jul 29

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

arXiv:2607. 24861v1 Announce Type: cross Abstract: Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities.

By Xin He, Yili Wang, Wenqi Fan, Qing Li, Qinggang Zhang, Yi Chang, Xin Wang

rag nlp multimodal

arXiv AI

Jul 29

What Gets Lost When Memory Becomes Media? Evaluating AI-Generated Oral History Visualization

arXiv:2607. 24756v1 Announce Type: cross Abstract: What gets lost when memory becomes media?

By Kwangsuk Park, Jaehyun Koo, Jiyeon Lee, Anjung Tan, Hyoungchul Park

agents nlp

arXiv AI

Jul 29

On the Use of LLMs for Specialised Terminology: A Good Alternative to Corpora?

arXiv:2607. 24784v1 Announce Type: new Abstract: Specialised translation relies on the use of documentary and terminological resources, including corpora.

By Joachim Minder (ALTAE), Guillaume Wisniewski (LLF - UMR7110), Natalie K\"ubler (ALTAE)

llms nlp

arXiv AI

Jul 29

Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook

arXiv:2509. 16780v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials.

By Eason Chen, Chuangji Li, Eric Li, Zimo Xiao, Jionghao Lin, Kenneth R. Koedinger

llms rag nlp safety

arXiv AI

Jul 29

Architectural Backdoors in Vision-Language Model Supply Chains via Representation Steering

arXiv:2607. 25479v1 Announce Type: cross Abstract: Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text encoders, and exported computation graphs are distributed by third parties and reused across downstream services.

By Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cin\`a, Luca Oneto, Iacopo Masi, Fabio Roli

llms rag diffusion nlp fine-tuning multimodal safety

arXiv AI

Jul 29

A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series

arXiv:2607. 25947v1 Announce Type: new Abstract: Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications.

By Frank Nie, Ethan B Liu, Yuan Zhu, Wei Fan, Jindong Han

llms rag nlp efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Jul 28

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.

llms rag nlp benchmarks

arXiv AI

Jul 28

Neural Machine Translation for Low-Resource Tangkhul--English

arXiv:2606. 25365v2 Announce Type: replace-cross Abstract: We present a study on low-resource machine translation for the Tangkhul-English (nmf-en) language pair.

By Chormi Zimik Vashai, Agniva Maiti

nlp fine-tuning safety

arXiv AI

Jul 28

An empirical investigation into the properties of standard word embeddings

arXiv:2607. 23675v1 Announce Type: cross Abstract: The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past.

By Salomon Kabongo

rag nlp multimodal

arXiv AI

Jul 28

Explaining BiomedCLIP with Weighted Banzhaf Interactions Supported by Tree-Gram Parsing

arXiv:2607. 23368v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are demonstrating significant capabilities in medical tasks like radiology analysis, yet providing faithful and interpretable explanations remains a key consideration for their responsible deployment in clinical settings.

By Jakub Rymarski (University of Warsaw, Poland), Adam Rempa{\l}a (University of Warsaw, Poland), Bart{\l}omiej Sobieski (University of Warsaw, Poland), Przemys{\l}aw Biecek (University of Warsaw, Poland)

llms nlp multimodal