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Task-Awareness Improves LLM Generations and Uncertainty

Tim Tomov, Dominik Fuchsgruber, Stephan Günnemann

TL;DR

Task-Awareness Improves LLM Generations and Uncertainty introduces a general framework that maps LLM outputs into a task-dependent latent space $\mathcal{L}$ via $g_T$, enabling Minimum Bayes Risk decoding in the latent space and a task-aligned uncertainty measure. By estimating $p(\ell|x)$ with Monte Carlo samples and using a task-specific dissimilarity $d_T$, the approach synthesizes Bayes-optimal latent responses $\ell_{Bayes}$ that often outperform beam search across single-label, multi-label, semantic-embedding, knowledge-graph, and simplex-valued tasks. Uncertainty is quantified via Bayes risk and Wasserstein distance to the ground-truth latent distribution, yielding predictions that align more closely with actual task performance than standard uncertainty estimators. The framework is broad and can be applied to any problem with a latent response structure, enabling reliable, task-aware predictions across diverse downstream applications.

Abstract

In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decoding and uncertainty estimation methods operate only in language space and largely disregard structural information. We address this by modeling LLM outputs directly in a task-dependent latent structure. By equipping this structure with a dissimilarity measure, we can compute Bayes-optimal responses. These are not selected from sampled generations but are newly synthesized by combining individual responses in the latent space. Across different tasks, Bayes-optimal responses consistently outperform standard decoding methods like beam search. Moreover, quantifying uncertainty via the induced Bayesian risk captures variations in terms of the latent structure and improves alignment with output quality and correctness. Our decision-theoretic framework is applicable to any problem that admits a latent response structure and enables reliable task-aware LLM predictions.

Task-Awareness Improves LLM Generations and Uncertainty

TL;DR

Task-Awareness Improves LLM Generations and Uncertainty introduces a general framework that maps LLM outputs into a task-dependent latent space via , enabling Minimum Bayes Risk decoding in the latent space and a task-aligned uncertainty measure. By estimating with Monte Carlo samples and using a task-specific dissimilarity , the approach synthesizes Bayes-optimal latent responses that often outperform beam search across single-label, multi-label, semantic-embedding, knowledge-graph, and simplex-valued tasks. Uncertainty is quantified via Bayes risk and Wasserstein distance to the ground-truth latent distribution, yielding predictions that align more closely with actual task performance than standard uncertainty estimators. The framework is broad and can be applied to any problem with a latent response structure, enabling reliable, task-aware predictions across diverse downstream applications.

Abstract

In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decoding and uncertainty estimation methods operate only in language space and largely disregard structural information. We address this by modeling LLM outputs directly in a task-dependent latent structure. By equipping this structure with a dissimilarity measure, we can compute Bayes-optimal responses. These are not selected from sampled generations but are newly synthesized by combining individual responses in the latent space. Across different tasks, Bayes-optimal responses consistently outperform standard decoding methods like beam search. Moreover, quantifying uncertainty via the induced Bayesian risk captures variations in terms of the latent structure and improves alignment with output quality and correctness. Our decision-theoretic framework is applicable to any problem that admits a latent response structure and enables reliable task-aware LLM predictions.
Paper Structure (49 sections, 10 theorems, 58 equations, 8 figures, 7 tables)

This paper contains 49 sections, 10 theorems, 58 equations, 8 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Latent Structures Enable Better LLM Generations and Uncertainty Quantification
  3. Structure-Aware Minimum Bayes Risk Decoding
  4. Approximating the Distribution over Latent Responses
  5. Uncertainty Quantification Through Bayes Risk
  6. Unambiguous Ground-Truth
  7. Examples of Latent Spaces and Metrics
  8. Single-Label Classification
  9. Multi-Label Classification / Prediction Sets
  10. Semantic Embedding Spaces
  11. Knowledge Graphs
  12. Ambiguous Ground-Truth: Probability Simplex
  13. Experiments
  14. Experimental Setup
  15. Tasks and latent structures.
  16. ...and 34 more sections

Key Result

Lemma 3.0

For any given class $\ell \in [K]$, let $p_\ell = \Pr(g_T(s) = \ell)$ denote the relative frequency among the MC samples. Under exact-match loss $d_T(\hat{\ell}, \ell)=\mathbf{1}\{\hat{\ell} \neq \ell\}$, a Bayes-optimal action is any mode of $p$, The corresponding Minimum Bayes Risk is

Figures (8)

  • Figure 1: Framework of embedding LLM responses $s \mid x$ into a task-dependent latent space $\mathcal{L}$ on the example of set-based multi-answer question answering. The Bayes-optimal $\ell_\text{Bayes}$ response is the centroid in the latent space w.r.t. a distance metric $d_T$. It does not need to be generated by the LLM directly. Uncertainty is quantified as Bayesian risk that measures the spread in $p(\ell \mid x)$ w.r.t. to $d_T$.
  • Figure 2: Improvement (Hamming distance) of our Bayes-optimal prediction over each other decoding baseline for set-based multi-answer QA (MAQA) vs. the entropy of the push-forward latent distribution $p(\ell \mid x)$. Under high variability, we synthesize answers that are substantially different from other outputs.
  • Figure 3: Performance in Hamming Distance of $\ell_{Bayes}$ estimator on Multi-Answer QA over an increasing numbers of MC samples.
  • Figure 4: UQ performance of $R(\ell_{Bayes})$ on Multi-Answer QA over an increasing numbers of MC samples.
  • Figure 5: UQ performance of $R(\ell_{Beam})$ on Multi-Answer QA over an increasing numbers of MC samples.
  • ...and 3 more figures

Theorems & Definitions (15)

  • Lemma 3.0
  • Lemma 3.0
  • Lemma 3.0
  • Lemma 3.0
  • Theorem 3.1
  • Lemma 4.0
  • proof
  • Lemma 4.0
  • proof
  • Lemma 4.0
  • ...and 5 more