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Found 23 papers, 6 papers with code
Automatic Verification of Data Summaries
We present a generic method to compute thefactual accuracy of a generated data summarywith minimal user effort.
Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities.
Neural Machine Translation for Fact-checking Temporal Claims
Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources.
Combating Misinformation in the Arab World: Challenges & Opportunities
Misinformation and disinformation pose significant risks globally, with the Arab region facing unique vulnerabilities due to geopolitical instabilities, linguistic diversity, and cultural nuances.
RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models
Current benchmarks often assess short factual answers, overlooking the critical ability to generate structured, multi-record tabular outputs from parametric knowledge.
Community Moderation and the New Epistemology of Fact Checking on Social Media
Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content.
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL
To this end, it considers the following LLM settings: (1) ZSL, including general-purpose reasoning or not; (2) SFT, with and without task-specific reasoning traces; (3) RL, exploring the use of different rewarding functions, both the established EXecution accuracy (EX) and a mix with fine-grained ones that also account the precision, recall, and cardinality of partially correct answers; (4) SFT+RL, i. e, a two-stage approach that combines SFT and RL.
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning
Incorporating external knowledge in large language models (LLMs) enhances their utility across diverse applications, but existing methods have trade-offs.
An LLM-Based Approach for Insight Generation in Data Analysis
Generating insightful and actionable information from databases is critical in data analysis.
Latent Abstractions in Generative Diffusion Models
In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of latent abstractions, that guide the generative process.
Finch: Prompt-guided Key-Value Cache Compression
Only such pairs are stored in the KV cache, which, within the space constrained by the context window, ultimately contains a compressed version of the long text.
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables.
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
For this optimisation, we introduce sparse regularisation and propose two methods for dealing with the issue of selecting an appropriate regularisation parameter.
Querying Large Language Models with SQL
Thus, we envision the use of SQL queries to cover a broad range of data that is not captured by traditional databases by tapping the information in LLMs.
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Fact-checking is one of the effective solutions in fighting online misinformation.
Unsupervised Matching of Data and Text
Our method builds a fine-grained graph over the content of the corpora and derives word embeddings to represent the objects to match in a low dimensional space.
RuleBert: Teaching Soft Rules to Pre-trained Language Models
While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge.
Automated Fact-Checking for Assisting Human Fact-Checkers
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules.
Local Embeddings for Relational Data Integration
The embeddings are learned based on such sentences.
Explainable Fact Checking with Probabilistic Answer Set Programming
One challenge in fact checking is the ability to improve the transparency of the decision.
Robust Discovery of Positive and Negative Rules in Knowledge-Bases
We present RUDIK, a system for the discovery of declarative rules over knowledge-bases (KBs).
Query-limited Black-box Attacks to Classifiers
Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score.
