4 papers
Extending LLM Context via Associative Recurrent Memory
Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8
Extending the context length of large language models (LLMs) is critical for many real-world applications, yet standard transformers remain constrained by quadratic compute and lin…
Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads
Artem Vazhentsev, Lyudmila Rvanova, Gleb Kuzmin +8
While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ)…
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models
Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny +4
Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). To date, information-based and consistency-based UQ have b…
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev +12
The rapid proliferation of large language models (LLMs) has stimulated researchers to seek effective and efficient approaches to deal with LLM hallucinations and low-quality output…