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Found 62 papers, 28 papers with code
Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study
Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context.
Conversation-Aware Filtering of Online Patient Forum Messages
We experiment with two approaches to add conversational context to a BERT model: a sequential CRF layer and manually engineered features.
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.
FuzzyBIO: A Proposal for Fuzzy Representation of Discontinuous Entities
Using FuzzyBIO also improves end-to-end performance for continuous and composite entities in two of three data sets.
Controlled Retrieval-augmented Context Evaluation for Long-form RAG
CRUX uses question-based evaluation to assess RAG's retrieval in a fine-grained manner.
Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
To address these limitations, we propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds through a self-incentivized process.
Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory
Previous approaches mainly follow a discriminative paradigm, utilizing shared encoders to process input features and task-specific heads to perform each task.
Improving RAG for Personalization with Author Features and Contrastive Examples
We introduce a new feature called Contrastive Examples: documents from other authors are retrieved to help LLM identify what makes an author's style unique in comparison to others.
Evaluation of Attribution Bias in Retrieval-Augmented Large Language Models
Our results show that adding authorship information to source documents can significantly change the attribution quality of LLMs by 3% to 18%.
QUIDS: Query Intent Generation via Dual Space Modeling
In this paper, we address the task of query intent generation: to automatically generate detailed and precise intent descriptions for search queries using relevant and irrelevant documents given a query.
Undesirable Memorization in Large Language Models: A Survey
We conclude our overview by identifying potential research topics for the near future: to develop methods for balancing performance and privacy in LLMs, and the analysis of memorization in specific contexts, including conversational agents, retrieval-augmented generation, multilingual language models, and diffusion language models.
Generate then Refine: Data Augmentation for Zero-shot Intent Detection
Our results indicate that a two-step approach of a generative LLM in zero-shot setting and a smaller sequence-to-sequence model can provide high-quality data for intent detection.
What are the limits of cross-lingual dense passage retrieval for low-resource languages?
Our results on the MKQA and AmQA datasets show that language alignment brings improvements to mDPR for the low-resource languages, but the improvements are modest and the results remain low.
Generative Retrieval with Few-shot Indexing
Existing generative retrieval (GR) approaches rely on training-based indexing, i. e., fine-tuning a model to memorise the associations between a query and the document identifier (docid) of a relevant document.
Reasoning with Large Language Models, a Survey
The field started with the question whether LLMs can solve grade school math word problems.
Efficient course recommendations with T5-based ranking and summarization
In this paper, we implement and evaluate a two-stage retrieval pipeline for a course recommender system that ranks courses for skill-occupation pairs.
The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
We conclude that it is possible to utilize RAG with quantized smaller LLMs.
Chain of Tools: Large Language Model is an Automatic Multi-tool Learner
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, empowering them to solve practical tasks.
Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus
We use MedRoBERTa. nl as base model and perform second-phase pretraining through self-alignment on a Dutch biomedical ontology extracted from the UMLS and Dutch SNOMED.
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics.
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems
In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection.
High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System
We show these challenges with log data from a live legal search system and two user studies.
Measuring Bias in a Ranked List using Term-based Representations
With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.
Learning to Use Tools via Cooperative and Interactive Agents
To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately.
Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs
Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID.
A Multi-Agent Conversational Recommender System
First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents.
Answer Retrieval in Legal Community Question Answering
Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain.
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?
ChatGPT, GPT-3. 5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks.
ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science
ChiSCor was compiled for studying how children render character perspectives, and unravelling language and cognition in development, with computational tools.
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
We further find that gains provided by AL strategies come at the expense of more assessments (thus higher annotation costs) and AL strategies underperform random selection when comparing effectiveness given a fixed annotation cost.
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks.
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts
We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of models fine-tuned on LLM-generated and human-generated data.
Political corpus creation through automatic speech recognition on EU debates
We release the resulting corpus and our analysis pipeline for future research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker
Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data, and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available.
Injecting the BM25 Score as Text Improves BERT-Based Re-rankers
In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as a token in the middle of the input of the cross-encoder re-ranker.
On the Interpolation of Contextualized Term-based Ranking with BM25 for Query-by-Example Retrieval
In this work, we examine the generalizability of two of these deep contextualized term-based models in the context of query-by-example (QBE) retrieval in which a seed document acts as the query to find relevant documents.
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Robust test collections are crucial for Information Retrieval research.
LeiBi@COLIEE 2022: Aggregating Tuned Lexical Models with a Cluster-driven BERT-based Model for Case Law Retrieval
Our methodology consists of four steps; in detail, given a legal case as a query, we reformulate it by extracting various meaningful sentences or n-grams.
The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation
During the COVID-19 pandemic, large amounts of COVID-19 misinformation are spreading on social media.
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization
Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular common in professional search tasks.
Expert Finding in Legal Community Question Answering
In the legal domain, there is a large knowledge gap between the experts and the searchers, and the content on the legal QA websites consist of a combination formal and informal communication.
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task.
Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics.
Small data problems in political research: a critical replication study
In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos & Whitford (A&W) propose a text classification method for tweets related to organizational reputation.
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack
Experimental results show that BERT models are vulnerable to variation in the entity context with 20. 2 to 45. 0% of entities predicted completely wrong and another 29. 3 to 53. 3% of entities predicted wrong partially.
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval.
Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain
We also investigate ensemble methods for combining multiple BERT models, and combining the best BERT model with a domain thesaurus using Conditional Random Fields (CRF).
Helping results assessment by adding explainable elements to the deep relevance matching model
This indicates that the explainability of the search engine result page leads to a better user experience.
Usability Evaluation for Online Professional Search in the Dutch Archaeology Domain
We conducted a user study for the evaluation of AGNES's search interface, with a small but diverse user group.
Improving reference mining in patents with BERT
In this paper we build on prior work using Conditional Random Fields (CRF) and Flair for reference extraction.
Named Entity Recognition for Chinese biomedical patents
There is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts.
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering
In this work, we train a GPT-2 language model to generate three distractors for a given question and text context, using the RACE dataset.
Creating a Dataset for Named Entity Recognition in the Archaeology Domain
In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain.
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study
Challenges of Applying Automatic Speech Recognition for Transcribing EUParliament Committee Meetings: A Pilot StudyHugo de Vos and Suzan VerberneInstitute of Public Administration and Leiden Institute of Advanced Computer Science, Leiden Universityh. p. de. vos@fgga. leidenuniv. nl, s. verberne@liacs. leidenuniv. nlAbstractWe tested the feasibility of automatically transcribing committee meetings of the European Union parliament with the use of AutomaticSpeech Recognition techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviews
We evaluated the effectiveness of using language models, that were pre-trained in one domain, as the basis for a classification model in another domain: Dutch book reviews.
Transfer Learning for Health-related Twitter Data
Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data.
Lexical Normalization of User-Generated Medical Text
In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature.
Information search in a professional context - exploring a collection of professional search tasks
Search conducted in a work context is an everyday activity that has been around since long before the Web was invented, yet we still seem to understand little about its general characteristics.
