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Found 18 papers, 5 papers with code
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations
We find that ``verbal uncertainty'' is governed by a single linear feature in the representation space of LLMs, and show that this has only moderate correlation with the actual ``semantic uncertainty'' of the model.
LUNAR: LLM Unlearning via Neural Activation Redirection
Large Language Models (LLMs) benefit from training on ever larger amounts of textual data, but as a result, they increasingly incur the risk of leaking private information.
Robust LLM safeguarding via refusal feature adversarial training
Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses.
Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
Our method improves performance by 25. 51% for TQA on WikiSQL and 22. 57% for MHQA on HotPotQA compared to the fine-tuned baselines.
PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs
Recently, machine unlearning, which seeks to erase specific data stored in the pre-trained or fine-tuned models, has emerged as a crucial protective measure for LLMs.
Know When To Stop: A Study of Semantic Drift in Text Generation
Overall, our methods generalize and can be applied to any long-form text generation to produce more reliable information, by balancing trade-offs between factual accuracy, information quantity and computational cost.
Spectral Filters, Dark Signals, and Attention Sinks
Projecting intermediate representations onto the vocabulary is an increasingly popular interpretation tool for transformer-based LLMs, also known as the logit lens.
Multilingual End to End Entity Linking
Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
Toolformer: Language Models Can Teach Themselves to Use Tools
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing
This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, that is entities without a description in the knowledge base and labeled mentions, have to be integrated into an existing entity linking system.
Multilingual Autoregressive Entity Linking
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Ranked #2 on
Entity Disambiguation
on Mewsli-9
(using extra training data)
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.
Task-Driven Linguistic Analysis based on an Underspecified Features Representation
In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.
