Challenge: Existing methods to estimate uncertainty use predictive confidence, structural characteristics of representation space, or stochastic variation in model outputs.
Approach: They propose a new uncertainty estimation framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples.
Outcome: The proposed framework outperforms or matches existing methods on several NLU benchmarks and sentiment analysis benchmarks.

Similar Papers

Combating the Curse of Multilinguality in Cross-Lingual WSD by Aligning Sparse Contextualized Word Representations (2022.naacl-main)

Copied to clipboard

Challenge: Existing approaches to handle knowledge acquisition bottlenecks in multilingual training are limited due to the curse of multilinguality.
Approach: They propose to use large pre-trained monolingual language models in cross lingual zero-shot word sense disambiguation coupled with a contextualized mapping mechanism.
Outcome: The proposed model improves the average F-score by nearly 6.5 points over 17 target languages.
UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Copied to clipboard

Challenge: Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach: They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome: The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated.
Approach: They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty.
Outcome: The proposed framework improves performance and efficiency over multiple tasks over multiple datasets.
Weight-Aware Activation Sparsity with Constrained Bayesian Optimization Scheduling for Large Language Models (2025.emnlp-main)

Copied to clipboard

Challenge: Existing activation sparsification methods rely on activation magnitude and weights for sparsity . authors propose a weight-aware activation-a-ware framework for large language models .
Approach: They propose a weight-aware activation sparsity framework that uses weight-based scoring to measure activation importance in sparsification and a custom GPU sparse kernel to support it.
Outcome: The proposed framework outperforms existing methods at 60% model-level sparsity and significantly outperfies them at higher sparsities.
Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations (2020.emnlp-main)

Copied to clipboard

Challenge: Using sparse word embeddings is highly applicable for word sense disambiguation (WSD) .
Approach: They propose an overcomplete set of semantic basis vectors that allows for sparse word representations.
Outcome: The proposed framework achieves an aggregated F score of 78.8 over five standard word sense disambiguating benchmark datasets.
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (2022.findings-emnlp)

Copied to clipboard

Challenge: Using low-resource languages, we assess the quality of uncertainty estimates from a wide array of approaches, but with more data.
Approach: They train models on sub-sampled datasets in three different languages to assess the confidence of a neural classifier.
Outcome: The proposed models train on sub-sampled datasets in three different languages and show that the quality of uncertainty estimates suffers with more data.
Why Uncertainty Estimation Methods Fall Short in RAG: An Axiomatic Analysis (2025.findings-acl)

Copied to clipboard

Challenge: Existing UE methods cannot reliably estimate the correctness of LLM responses in Retrieval-Augmented Generation (RAG) . Existing methods generate low uncertainty values without considering relevance of context to query .
Approach: They propose an axiomatic framework to identify deficiencies in existing UE methods and introduce five constraints that an effective UE method should meet after incorporating retrieved documents into the LLM’s prompt.
Outcome: The proposed framework satisfies all the axioms and improves correlation between uncertainty estimates and correctness.
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)

Copied to clipboard

Challenge: Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution.
Approach: They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense.
Outcome: The proposed model reflects data uncertainty satisfactorily but underestimates model uncertainty.
Uncertainty-Aware Test-Time Search for Optimization Problem Solving (2026.acl-long)

Copied to clipboard

Challenge: Language model hallucinations and limited availability of labeled datasets often result in misaligned formulations, code errors and feasibility failures.
Approach: They propose a Monte Carlo Tree Search framework that automates optimization problems from natural language descriptions with efficiency and reliability.
Outcome: The proposed framework achieves state-of-the-art solution accuracy and reduces token usage.
Do not Abstain! Identify and Solve the Uncertainty (2025.acl-long)

Copied to clipboard

Challenge: Existing solutions rely on evasive responses when confronting uncertain scenarios.
Approach: They propose a benchmark to assess LLMs' ability to recognize and address uncertainty . they generate context-aware inquiries that highlight the confusing aspect of the original query .
Outcome: Experiments with ConfuseBench show that LLMs struggle to identify root cause of uncertainty and solve it.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations