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High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLMshallucinationabstention
Abstract:

Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -- a problem known as hallucination. We instead propose post-training an LLM to generate content only when confident in its correctness and to otherwise (partially) abstain. Specifically, our method, HALT, produces capability-aligned post-training data that encodes what the model can and cannot reliably generate. We generate this data by splitting responses of the pretrained LLM into factual fragments (atomic statements or reasoning steps), and use ground truth information to identify incorrect fragments. We achieve capability-aligned finetuning responses by either removing incorrect fragments or replacing them with "Unsure from Here" -- according to a tunable threshold that allows practitioners to trade off response completeness and mean correctness of the response's fragments. We finetune four open-source models for biography writing, mathematics, coding, and medicine with HALT for three different trade-off thresholds. HALT effectively trades off response completeness for correctness, increasing the mean correctness of response fragments by 15% on average, while resulting in a 4% improvement in the F1 score (mean of completeness and correctness of the response) compared to the relevant baselines. By tuning HALT for highest correctness, we train a single reliable Llama3-70B model with correctness increased from 51% to 87% across all four domains while maintaining 53% of the response completeness achieved with standard finetuning.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes HALT, a capability-aligned finetuning method that trains LLMs to abstain from generating content when uncertain, operating at the fragment level (atomic statements or reasoning steps). It resides in the 'Fragment-Level Capability Alignment' leaf, which contains only this paper as a sibling. This leaf sits within the broader 'Capability-Aligned and Abstention-Based Finetuning' branch, which includes one other leaf ('Explicit Uncertainty Expression Training'). The sparse population of this specific leaf suggests the fragment-level granularity represents a relatively unexplored direction within abstention-based approaches.

The taxonomy reveals four other major branches addressing hallucination mitigation: behavioral/faithfulness-oriented finetuning, retrieval-augmented generation, vision-language models, and mechanistic studies of finetuning data. HALT's abstention-based approach contrasts with neighboring behavioral methods that improve faithfulness without explicit refusal mechanisms, and with RAG approaches that rely on external evidence rather than internal capability assessment. The fragment-level granularity distinguishes HALT from the sibling 'Explicit Uncertainty Expression Training' leaf, which focuses on whole-response abstention phrases like 'I don't know' rather than partial content removal or replacement.

Among 30 candidates examined, two contributions show potential prior work overlap. The core HALT method (10 candidates examined, 1 refutable) and the tunable trade-off mechanism (10 candidates examined, 1 refutable) each have one candidate suggesting overlapping ideas, though the analysis does not indicate whether these represent substantial precedent or incremental differences. The data generation pipeline (10 candidates examined, 0 refutable) appears more distinctive within this limited search scope. The statistics suggest moderate novelty for the first two contributions and stronger novelty for the third, though the 30-candidate scope leaves open the possibility of additional relevant work beyond the top semantic matches.

Based on the limited search scope of 30 semantically similar papers, HALT appears to occupy a sparsely populated research direction within the broader hallucination mitigation landscape. The fragment-level granularity and capability-aligned data generation represent distinguishing features, though the presence of refutable candidates for two contributions indicates some conceptual overlap with prior work. A more exhaustive literature search would be needed to definitively assess novelty across the full spectrum of abstention-based and hallucination mitigation research.

Taxonomy

Core-task Taxonomy Papers
15
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: Reducing hallucinations through capability-aligned language model finetuning. The field addresses the challenge of ensuring that language models produce factual, grounded outputs by aligning their behavior with their actual knowledge and capabilities. The taxonomy reveals five main branches that capture distinct mitigation strategies. Capability-Aligned and Abstention-Based Finetuning focuses on teaching models to recognize and abstain from answering when uncertain or when queries exceed their knowledge boundaries, exemplified by works like Honest AI[11] and Scepticism Modeling[8]. Behavioral and Faithfulness-Oriented Finetuning emphasizes training models to produce outputs that remain faithful to their internal representations and avoid fabricating information, as seen in Faithful Finetuning[9]. Retrieval-Augmented Generation Finetuning integrates external knowledge sources during training to ground responses in verifiable evidence, illustrated by RAGTruth[3] and Finetune RAG[15]. Vision-Language Model Hallucination Mitigation addresses multimodal settings where visual and textual modalities must be aligned, with approaches like Detecting Vision Hallucinations[5] and Aligning Modalities Preference[2]. Finally, Finetuning Data and Knowledge Mechanisms investigates how training data quality and knowledge representation affect hallucination rates, explored in studies such as Unfamiliar Examples Hallucinate[7] and Knowledge Awareness Hallucinations[14]. A particularly active line of work contrasts abstention-based methods, which teach models to decline answering, with faithfulness-oriented approaches that aim to improve answer quality without explicit refusal mechanisms. HALT[0] sits within the Capability-Aligned and Abstention-Based Finetuning branch, specifically targeting fragment-level capability alignment—a fine-grained approach that goes beyond whole-question abstention. Compared to broader abstention strategies like Honest AI[11], HALT[0] operates at a more granular level, aligning model outputs with knowledge at the sub-answer scale. This contrasts with retrieval-augmented methods such as RAGTruth[3], which rely on external evidence rather than internal capability assessment. The tension between teaching models when not to answer versus improving their intrinsic faithfulness remains a central open question, with HALT[0] contributing a middle path that preserves informativeness while reducing hallucinations through localized alignment.

Claimed Contributions

HALT: Capability-aligned finetuning method for reliable LLMs

The authors introduce HALT, a finetuning method that trains LLMs to generate responses only within their capability limits by splitting responses into factual fragments, identifying incorrect fragments using ground truth information, and either removing them or replacing them with 'Unsure from Here'. This enables models to abstain when uncertain rather than hallucinate.

10 retrieved papers
Can Refute
Tunable trade-off mechanism between response completeness and correctness

HALT provides a tunable threshold parameter that allows practitioners to control the balance between how complete responses are versus how correct individual fragments are, enabling deployment-specific customization of model behavior across different risk tolerance scenarios.

10 retrieved papers
Can Refute
Capability-aligned finetuning data generation pipeline

The authors develop a pipeline that generates finetuning data aligned with the pretrained model's capabilities by few-shot prompting the model, fragmenting responses, evaluating fragment correctness with an evaluator LLM using ground truth, and post-processing to create responses containing only fragments the model can reliably generate.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

HALT: Capability-aligned finetuning method for reliable LLMs

The authors introduce HALT, a finetuning method that trains LLMs to generate responses only within their capability limits by splitting responses into factual fragments, identifying incorrect fragments using ground truth information, and either removing them or replacing them with 'Unsure from Here'. This enables models to abstain when uncertain rather than hallucinate.

Contribution

Tunable trade-off mechanism between response completeness and correctness

HALT provides a tunable threshold parameter that allows practitioners to control the balance between how complete responses are versus how correct individual fragments are, enabling deployment-specific customization of model behavior across different risk tolerance scenarios.

Contribution

Capability-aligned finetuning data generation pipeline

The authors develop a pipeline that generates finetuning data aligned with the pretrained model's capabilities by few-shot prompting the model, fragmenting responses, evaluating fragment correctness with an evaluator LLM using ground truth, and post-processing to create responses containing only fragments the model can reliably generate.

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