Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Authors: Yuehan Qin
Li Li
Yi Nian
Xinyan Velocity Yu
Yue Zhao
Xuezhe Ma
Venue: Accepted by TMLR ()

Abstract: Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially when a user query includes one or more false premises—claims that contradict established facts. Such premises can mislead LLMs into offering fabricated or misleading details. Existing approaches include pretraining, fine-tuning, and inference-time techniques that often rely on access to logits or address hallucinations after they occur. These methods tend to be computationally expensive, require extensive training data, or lack proactive mechanisms to prevent hallucination before generation, limiting their efficiency in real-time applications. We propose a retrieval-based framework that identifies and addresses false premises before generation. Our method first transforms a user’s query into a logical representation, then applies retrieval-augmented generation (RAG) to assess the validity of each premise using factual sources. Finally, we incorporate the verification results into the LLM’s prompt to maintain factual consistency in the final output. Experiments show that this approach effectively reduces hallucinations, improves factual accuracy, and does not require access to model logits or large-scale fine-tuning.

Published in Transactions on Machine Learning Research (02/2026)
Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Yuehan Qin
yuchangiiuusc.edu
University of Southern California
Shawn Li
k..i02 edu
University of Southern California
Yi Nian
yinion @usc. edu
University of Southern California
Xinyan Vclocity Yu
minyang@use.co edu
University of Southern California
Yue "hao'
yue.:Ousc.com edu
University of Southern California
Xuezhe Ma*
zuezhema@usc.ce
University of Southern California
Reviewed on OpenReview:
https: optnr/weewwww... net/ forum? id= BD=St
Abstract
Large language models (LLMs) have shown substantial capacity for generating fluent, textually appropriate responses. However, they can produce hallucinated outputs, especially when a user query includes one or more false premises claims that contradict established facts. Such premises can mislead LLMs into offering fabricated or mislcading dotails. Ex- isting approaches include pretraining, finettuiin, and inference-time techniques that often rely on access logits or address hallucinations after they occur. These methods tend to be computationally expensive, require extensive training data, or lack proactive mechanisms to prevent hallucination before generation, limiting their efficiency in real-time applications. We propose a retrieval-based framework that identifies and addresses false premises before generation. Our method first transforms user's query into a logical representation, then applies retrieval-augmmented generation (RAG) to assess the validity of each premise using factual sources. Finally, we incorporate the verification results into the LLM's prompt to maintain factual consistency in the final output. Experiments show that this approach ef- fectively reduces hallucinations, improves factual accuracy, and does not require access to model logits large-scale fine-tuning.
1 Introduction
Large Language Models (LLMs) generate fluid, context-aware responses but can produce hallucinations when
prompted with queries that include hidden factual errors (Manakul al.) 2023 Zheng et al. 2023) These
*Corresponding authors.

---

Published in Transactions on Machine Learning Research (02/2026)
errors, known false premises, statements in user's question that conflict with real-world facts. Even when LLMSs can store accurate information, they may trust the incorrect assumptions embedded in the query and generate misleading outputs (Yuan et al. 2024). This is especially problematic in sensitive applications such as finance or healthcare, where mistakes can cause serious harm (Pal et al. 2023
Prior research distinguishes between factuality hallucinations, where the output conflicts with known facts, and faithfulness hallucinations, where the response diverges from the provided context or user instructions (Snyder et al. 2024a). We focus factuality hallucinations, particularly those driven by incorrect assump- tions (false premises) These errors common among LLM outputs (Huang et al. 2025 Snyder et all, 2024b), where Fig. presents an example of a question with false premise and the resulting hallucina- tion. These premise-driven errors are particularly insidious as they can appear factually sound while being fundamentally incorrect.
Is penicillin safe for treating my shellfish allergy symptoms?
Yes, penicillin is generally safe for most people and is one of the most widely prescribed antibiotics. It has a good safety profile when used appropriately. For shellfish allergy symptoms, penicillin can help reduce inf lammation and prevent secondary bacterial infections that might occur from scratching or skin irritation
Figure 1: LLM experiences factuality hallucination when faced with false premise question, where both entities shellfish allergy symptom and penicillin exist but are not correctly aligned. The LLM'' hallucinated response could delay life-saving treatment by incorrectly recommending antibiotics for allergic reactions.
Many methods attempt to address false premises after an LLM has already produced an answer. They include fine-tuning the model detect invalid assumptions (Hu et al. 2023), applying contrastive decoding surface inconsistencies (Shi et al.] 2023 Chuang et al. (024)), and using uncertainty-basee measures logits to gauge inaccuracies (Pezeshkpour 2023 Varshney et al.) 2023). Although effective in some contexts, these approaches
be computationall demanding and do not necessarily prevent misinformation from appearing in the first place. Additionally, questions with false premises often maintain normal semantic flow, changing only a few tokens so that they are difficult to identify using traditional out-of-distribution detection (Vu et al. 2023). Even advanced LLMs can struggle with real-time truth evaluation, lacking the context or capacity to fully check every assumption (Hu el al. 2023 Liu et al. 2024c).
To address this challenge, we focus on preventing hallucinations rather than mitigating them post hoc. In our framework, first transform the user's qucry into logical form that highlights key entities relations. We then employ retrieval-augmented generation (RAG) to check the accuracy of these statements against knowledge graph. If contradictions are found, the query is flagged as containing a false premise prompting the model to correct or reject the assumption before formulating a final answer. This process, shown in Fig. 2 ensures that the LLM does not rely on erroncous details during response generation. By informing the LLM about any detected false premise advance, we reduce the likelihood of hallucinations without requiring access model logits largo-scale fine-tuning.
We summarize contributions follows:
We first introduce logical forms to represent input queries and demonstrate their effectiveness across various types of graph retrievers. This logical approach enables accurate and systematic evaluation of statements provided in user prompts, particularly handling queries that may include false premises.
Logical Form Representation:
Explicit False Premise Detection: Our method improves the reliability of LLM- LLM- generrtte responses by explicitly detecting false premises and informing the L.M if a question contains false premise.
2

---

Published in Transactions on Machine Learning Research (02/2026)
Hallucination Mitigation Without Output Generation or Model Logits: Our approach reduces
factual hallucinations without actual generation of responses or LLM logits and, therefore, can be seamlessly integrated into existing LLM frameworks and pipelines, offering a straightforwar enhancement for improving factual accuracy.
2
Related Works
False Premise. A False Premise Question (FPQ) is a question containing incorrect facts that are not necessarily explicitly stated but might be mistakenly believed by the questioner (Yu et al. 2022 Kim et al. 2021). Recent studies (Yuan et al. 2024 Li et al. 2024 2025ch have demonstrated that FPQs can induce factuality hallucination in LLMs, as they often respond directly to FPQs without verifying their validity. Notably, existing prompting techniques like few-shot prompting (Brown et al.
2020 and Chain-of-Thoughh
(Wei et al. 2023), tend to increase hallucinations. Converscly, directly prompting LLMs to detect falsc premises degrades their performance on questions containing valid premises
(uu et al. 2023
Logical Forms.
Symbolic solvers and logical forms applied to logical reasoning by grounding natural
language in symbolic reprosentations. The latest trend is integrating LLMs with symbolic solvers to enhance their performance Olausson et al. (2023): ; Pan et al. (2023a) ; Li et al. (2025b 2023), where natural language is translated into symbolic logic forms and deterministic symbollc solvers are employed for inference, enabling more accurate logical problem-solving. Similarly, SymbCoT Xu ct al. (2024) converts input text into symbolic formats such as first-order logic, generates reasoning plans through logical rule application, and verifies the reasoning process to ensure consistency. These methods demonstrate that incorporating symbolic improves the reliability and interpretability of LLM outputs, making them well-suited for tasks requiring logical consistency.
Knowledge Graph Fact Checking and Question Answering. In fact checking, RAG approaches verify data accuracy, with knowledge graph-driven RAG gaining attention for effectively leveraging structured knowledge. Recent works include: 1) prompt-based methods where (Pan et al.] 2023b) evaluates evidence sufficiency and generates verification questions, and (Sun et al. 2024) performs hop-by-hop fact retrieval; 2) graph-based approaches where {He et al. 2024) formulates RAG a Prizo-Collecting Steiner Tree problem for subgraph extraction, and (Mavromatis & Karypis 2024) uses graph neural networks for dense subgraph reasoning and answer retrieval; 3) training-based methods where (Zheng et al.) 2024] develops dual encoders for query and subgraph evidence embedding, and (Liu et al. 2024a) trains encoders for retrieval and ranking processes, though requiring entity presence in the knowledge graph and relying on poomptggenertte training data.
Hallucination Mitigation. Sources of LLM hallucinations originate from different stages in the LLM life cycle (Zhang et al. 2023a ei al. 2026 Shawn et al. 2025). leading existing mitigation methods to target
specific stages: 1)
Pre-training: Fnhaneing factual relia reliability by emphasixing credible texts, either by up-
sampling trustworthy documents (Touvron et al.) 2023 or prepending factual sentences with topic prefixes [Lee et al. 2023). 2) Supervised Fine-tuning: Curating high-quality, instruction-oriented datasets (Chen) et al. 2024 Cao et al. 2024) improves factual accuracy more effectively than fine-tuming min-tuming on unfiltered data, and remains more feasible compared to extensive pre-training- 3) Reinforcement Learning from Human Feedback: Aligning closely with human preferences may inadvertently encourage hallucinations or biased outputs, especially when instructions surpass the model's existing knowledge (Radhakrishnan et al. 2023 Wei et al. 2024). 4) Inference: Known hallucination snowballing (Zhang al.) 2023b),, LLMs occasion- ally magnify initial generation mistakes. Proposed inference-time solutions include new decoding strategies (Shi el al. 22233 Chuang el al. 2024), uncertainty analysis of model outputs (Xu & Ma 2025 Liu el al. 2024b Dhuliawala et al.) 2023 Li et al.) 2025a). However, these approaches either act post-hallucination or require access to model logits, thus being inefficient due to repeated prompting or limited whito-box LLM scenarios. We briefly discuss the comparison between our work and previous post-hoc hallucination mitigation method in Tab. 1 and detailed discussion can be found in Discussion and Appendix $ A.4
3

---

Published in Transactions on Machine Learning Research (02/2026)
Method
Training
Number of tokens
Training time
Model agnostic
Black-box Compatible
Post-hoc
Depends on fine-tuming
Train: original query + answer
Depends on
No
method
Inference: original query
fine-tuming Zero
Ours
Zero
Original query + logical form
Yes
Yes
Table 1: Comparison of training and compatibility between post-hoc method and our method.
Retriever
Was The Lord of the Rings: The Return of the King nominated for the Acadeny Award for Best Director?
Logical Form
Logical Form Embedding
Relevance Distribution
Knowledge
Graph
Logical Form
E_q
Query
Encoder
Relevance
Evidence
Retriever
E_e
Evidence Embedding
Top-k
Retrieved
Logical Form
Subgraph
Top-k similar triples
Nominated("The Lord of the Rings The Return the King","A "Acade my Award for Best Director")
Connected subgraph optimized by Prize-
Top
similar
nodes
Evidence
Collecting Steiner Tree
Triple
relations
Premise Verification
Source:
Original
The Lord of the Rings: The Return of the King
Query
Relationship: Nominated
Answer
Target:
Premise
Academy Award for Best Director
Verification
Figure 2: Overview of our approach. Left: The original query is converted into logical form. Middle: The
logical form is used to retrieve relevant elements from the knowledge graph and detect false premises. Right: Comparison of studied retrievers for aligning logical form with the knowledge graph. The LLM generates responses with reduced hallucination given prompts with premise verification.
3 Methodology
LLM hallucinations often stem from false premises in user queries. Instead of addressing hallucinations post-generation, we aim to prevent them by detecting and informing the presence of false premises to LLMs before response generation. Our proposed method achieves this through three key steps:
Logical Form Conversion. extract its core meaning, making casier to analyze its factual consistency. We demonstrate its effectiveness across various types of graph retrievers.
By converting the user query into a structured logical form representation, we
Structured Retrieval and Verification. Rather than relying solely model-generated text, retrieve external evidence to assess whether false premises exist in the query.
Factual Consistency Enforcement. The verified information is then incorporated into the LLM prompt. ensuring that the model generates responses aligned with factual data.
Our proposed method applies knowledge graphs and datasets compatible with graph structures. We show the pseudocode summary of approach in Algorithm 1

---

Published in Transactions on Machine Learning Research (02/2026)
Algorithm False premise detection and hallucination mitigation
Input: User query Knowledge graph G
Output: Hallucination mitigated response from LLM
1: Convert query q into lugical representation L(g)
2
2: Extract logical assertions "(11,22...,,))) from L(q)
3: Initialize maximum similarity score Simmuua
3.3
4: Initialize optimal graph G 0
5: Candidate
subsets relevant subgraphs from Le., R(G)
6: triple G' G
T: retriever embedding--asse then
8: Compute similarity via embeddings:
Sim Sim (E(x), G*)
9: else retriever non- parametric then
Compute similarity using search criteria:
Sim + PCST (f(q).
11:
else if retriever is LLM-based then
12:
Compute similarity using LLM scoring:
Sin LLMScore (L(g), G*)
13:
end if
14: If Sim > Simmar then
15:
Simmar + Sim
16:
G t G"
17:
end if
18:
end for
19: Define false premise indicator function:
$
1, if conflicts with retrieved evidence G Ríg, G*)
F(g)
0,
otherwise
20: if F(g) = then
33
21: Update query
9 Note: This question contains false premise."
22: end if
23: Generate response from LLM using updated query 9
24: return Hallucination mitigated response from LLM
3.1 Problem Definition
False Premise Detection: Given a user query g, the function F(q) determining whether q contains a false
premisc can be defined as:
1, if q conflicts with retrieved evidence R(g, G),
F(g)=
(1)
0, otherwise,
where R. denotes the retrieval function that extracts relevant evidence from knowledge graph G. The query is evaluated against R(g, G),, and if contradictions are found, is deemed to contain a false premiso (F(g) = 1); otherwise, it is considered valid (F(g) = 0). In this study, the function F' is achieved by RAG using a retriever that leverages logical form and a knowledge graph.
3.2 Logical Form Extraction
Logical Form: A logical form is a structured representation of statements or queries expressed using symbolic logic. It provides a structured way to capture semantic relationships within sentences, enabling precise and systematic reasoning. Given a natural language sentence query q, its logical form L(g) can be represented as: C(q) = P(x1, ....., "2 In), where P denotes predicate or relation, and æ Ø2 ...,,,,,, variables or constants representing entities or concepts extracted from g. For example, for the query:
......,......................... In are
Was The Lord of the Rings: The Return of the King nominated for the Academy Award for Best Director?
5

---

Published in Transactions on Machine Learning Research (02/2026)
Its logical form is:
Nominated(""Tn "The Lord of the Rings: The Return of the King", "Academy Award for Best Director")
Here, "The Lord of the Rings: The Return of the King" and "Academy Award for Best Director' " are entitios, while Nominated is the relation. GPT-4o-mini [ 2024 used for extracting logical forms from the queries. For an input query q. we first ask the LLM to generate the corresponding logical form L(g). Then, we extract the source, relationship, and target from L(g). The prompt for logical form conversion is included in Appendix $ A.3
To evaluate the quality of the logical form conversion, we ask two annotators to manually grade the generated logical forms using& a three-point scale: 1 (do not match), 2 (partially match), and 3 (match). Each annotator grades 100 randomly sampled outputs. Across all 200 samples, the generated logical forms receive a score of in all of the cases. More details are Appendix $A.6
3.3 Retrieval
Given user query in natural language, the retrieval stage aims to extract the most relevant elements (c.g.. entities, triplets, paths, subgraphs) from knowledge graphs, which can be formulated as:
G Graph--Retriverrge G)
arg max Pe(G q, G) GCR(G)
arg max Sim(g, G),
(2)
GCN(G)
where G* is the optimal retrieved graph elements, and Sim(+,) is function that measures the semantic similarity between user queries and the graph data. R(-)) represents function to narrow down the search range of subgraphs, considering the efficiency.
After converting a user query q into logical form representation L(g), the retriever encodes the logical form and the graph triples, searches through the knowledge graph G, and extracts the most relevant triple or subgraph, applying different selection criteria depending on the retriever used in our study. Therefore, formula 2 can be further formulated Lo:
G Graph-RRetriever((())..
arg max (C L(g), G) GCR(G)
arg max Sim(C(q),G). GCR(G)
(3)
We employ the pre-trained encoder all-roberla-large--/ representation L(g) is used in both the similarity- based retrieval process and the step where the LLM assesses whether the original query g contains false premiso.
to encode the logical form and graph triplets. The
3.4 Hallucination Mitigation
For given user query if the false premise identification function F(g) detects false premise (F(g) = 1), we update its original query q by appending a note:
q W. = g.
(4)
otherwise,
This question contains a false premise.', and g is the modified query that explicitly flags the presence of a false premise when detected. Once the original query is updated, we evaluate LLM's responses and measure the effectiveness of the ensuing hallucination mitigation.
where W 'Note:

---

Published in Transactions on Machine Learning Research (02/2026)
4 Experiments
4.1 Dataset
KG-FPQ (Zhu et al. 2024] is a dataset containing true and false premise questions that are constructed from the KoPL knowledge graph, high-quality subset of Wikidata. TPQs are generated from true triplets, while FPQs are created by replacing objects in false triplets via string matching. We evaluate the discriminative task in the art domain, where LLMs answer Yes-No questions (e.g., Is Hercules a cast member of 'The Lord of the Rings: the Return of the King"?). Dataset details are in Appendix AA.1
The CREAK dataset (Onoe et al. 2021 is benchmark for commonsense reasoning about entity knowledge. Unlike prior datasets focused on general physical or social scenarios, CREAK targets inferences that combine factual knowledge about specific entities with commonsense reasoning. It contains 13,000 human-authored English claims about entities labeled as true or false, along with a small contrast set. Each example requires understanding both factual attributes and commonsense implications. Dataset details are in Appendix $..2
The FEVER dataset Thorne al. 2018) consists of natural-language claims labeled as Supported, Refuted, or Not Enough Information. Evidence is retrieved from Wikipedia, which functions as natural-language knowledge basc Claims created by modifying Wikipedia sentences and are subscquently verified inde- pendently, without access to their original sources. FEVER is widely adopted as standard benchmark for fact verification and claim validation.
4.2 Experiment Setting
Our approach mitigates hallucination through a two-step process: First, we detect false premises in the user query. Then, use the result of false premise detection along with the original query when providing imput to the LLM. We use both the KG-FPQ dataset and the CREAK dataset for evaluating the premise detection task, and we use the KG-FPQ dataset for the hallucination mitigation task, since CREAK dataset contains statements, not questions.
4.2.1 False Premise Detection with Logical Form
In the false premise detection task, we look at different retrievers with and without the of logical forms. Logical forms used in 1) the retrieval stage, where the logical form L(g) is encoded to find the most relevant elements from knowledge graph G, and 2) the false premise detection stage, where the logical form is passed as input along with the retrieved evidence to LLM to determine whether the query contains false premise. The prompt detail is in Appendix A.3 We evaluate the use of logical forms in three configurations: 1) applying logical forms in both the retrieval stage and false premise detection stage, 2) using logical forms for retrieval and employing the original query for false premise detection, and 3) utilizing the original query for both stages. We further analyze the role of different components in the logical form through ablation studies in Appendix A.4.2
4.2.2 False Premise Detection Methods
We ovaluate how logical form impacts retrioval for false premisc detection across the following retrievers:
We directly query the LLM to determine whether the given question contains a false premise. The model is prompted with: Does the following question contain false premise? Answer with 'Yes' or 'No' only.
1) Direct Claim:
2) Embedding-based Retriever: with RAG selects the top- relevant triples from the knowledge graph based on the cosine similarity between the query embedding and the graph triple embedding.
3) Non-parametric Retriever: G-retriever (He et al. 2024) uses Prize- Collectinnn Steiner Tree algorithm for extracting relevant subgraph from the knowledge graph. It does not rely on a trained model with learnable parameters.
Thhi work focuses top-1 selection.

---

Published in Transactions on Machine Learning Research (02/2026)
4) LLM-based Retriever: GraphRAG/Ion (Edge al. 2025 Sun al 2024 asks the LLM to generate score between 0 and 100, indicating how helpful the generated answer is in answering the target question. The answers are sorted in descending order of helpfulness score and used to generate the final answer returned to the user.
5) SAC3 Zhang et al. (2024) a hallucination detection approach that identifies hallucinations by assessing semantic-aware cross- check consistency; which involves generating semantically equivalent question pertur- bations and performing cross- cross-model model response consistency verification. We include SAC3 as baseline for the KG-FPQ dataset.
We use GPT-4o-mini as the LLM in the false premise detection task. Additional evaluations on other LLMs are provided in Appendix $A.4.4] These retrievers are included because they enable relrieval without task- specific fine-tuning, making them more adaptable across different domains. Unlike training- based retrievers, which require labeled data and extensive computation, non-parametric retriever uses structured knowledge, embedding- based relriever utilizes pre-trained encoders transform queries and knowledge into shared vector space for efficient retrieval, and LLM- b-see retrieval leverages pre-trained language models' general- ization abilities. This setup evaluates the impact of logical forms retrieval efficiency without the overhead of model training.
Metrics. We evaluate the false premise detection task using TPR (true positive rate), TNR (true negative rate), FPR (false positive rate), FNR (false negative rate), Fl score, and accuracy of the model successfully identifying questions containing false premises or not. Here, a positive instance refers a question that contains false premise. Higher TPR indicates better detection of false premises.
Direct Claim with RAG G-retriever
GraphRAG/Te ToG
Original Query for Both Stages
True Positives (TP%)
44.44
33.33
88.89
8.89
True Negatives (TN%)
73.33
80.00
86.67
93.33
False Positives (FP%)
26.67
20.00
13.33
6.67
False Negatives (FN%)
55.56
66.67
11.11
91.11
F1 Score (%)
59.70
48.78
87.89
16.16
Accuracy (%)
69.20
73.33
88.57
81.27
Logical Form for Retricval and Original Query for False Premise Detection
True Positives (TP%)
44.44
37.78
82.22
8.89
True Negatives (TN%)
73.33
86.67
93.33
93.33
False Positives (FP%)
26.67
13.33
6.67
6.67
False Negatives (FN%)
55.56
62.22
17.78
91.11
Fl Score (%)
59.70
53.97
86.97
16.16
Accuracy (%)
69.20
79.69
83.81
81.27
Logical Form for Both Stages
True Positives (TP%)
44.44
60.00
94.44
8.89
True Negatives (TN%)
73.33
86.67
99.05
93.33
False Positives (FP%)
26.67
13.33
0.95
6.67
False Negatives (FN%)
55.56
40.00
5.56
91.11
F1 Score (%)
59.70
73.97
97.12
16.16
Accuracy (%)
69.20
82.86
95.24
81.27
Table 2: KG-FPQ dataset: comparison of performance metrics across different retrieval methods using logical forms and/or original queries.
4.2.3 Hallucination Mitigation Methods
Having used logical forms improve query structuring and false premise dotection, we wish illustrato how our logical form-based method further reduces hallucinations. We consider the following methods our hallucination mitigation baselines, which are all inference-time hallucination mitigation strategies that

---

Published in Transactions on Machine Learning Research (02/2026)
Direct Claim
with RAG
G-retriover GraphRAG ToG
Original Query for Both Stages
True Positives (TP%)
72.5
62.3
24.6
89.9
True Negatives (TN%)
89.7
86.8
92.6
91.2
False Positives (FP%)
10.3
13.2
7.4
8.8
False Negatives (FN%)
27.5
37.7
75.4
10.1
F1 Score (%)
79.4
71.1
37.4
90.5
Accuracy (%)
81.0
74.5
58.4
90.5
Logical Form for Retrieval and Original Query for False Premise Detection
True Positives (TP%)
72.5
76.8
36.2
89.9
True Negatives (TN%)
89.7
92.6
83.8
88.2
False Positives (FP%)
10.3
7.4
16.2
11.8
False Negatives (FN%)
27.5
23.2
63.8
10.1
F1 Score (%)
79.4
83.5
47.6
89.2
Accuracy (%)
81.0
84.7
59.9
89.1
Logical Form for Both Stages
True Positives (TP%)
72.5
88.4
92.8
92.8
True Negatives (TN%)
89.7
92.6
83.8
91.2
False Positives (FP%)
10.3
7.4
16.2
8.8
False Negatives (FN%)
27.5
11.6
7.2
7.2
F1 Score (%)
79.4
90.4
88.9
92.1
Accuracy (%)
81.0
90.5
88.3
92.0
Table 3: CREAK dataset: comparison performance metrics across different retricval methods using logical
forms and/or or original queries.
Direct
WRAG (with RAG)
GraphRAG/ToC
Orig
LF-Retr
LF-Both
True Positive (TP%)
18.8
96.1
94.1
94.1
88.2
True Negative (TN%)
98.5
61.2
83.7
83.7
83.7
False Positive (FP%)
1.5
38.8
16.3
16.3
16.3
False Negative (FN%)
81.2
3.9
5.9
5.9
11.8
F1 Score (%)
31.3
82.4
89.7
89.7
86.5
Accuracy (%)
58.4
79.0
89.0
89.0
86.0
Table 4: FEVER dataset:
comparison of performance metrics across different retrieval methods using logical forms and/or original queries. Since g-retriever graph-based retriever and does not apply to non-graph data, we do not include it here.
Method
Accuracy 81.8
Number of tokens
Running lime"
Model agnostic
Black-box Compatible
Contrastive
Original Query + Reasoning Step
Context Retrieval Time
Agnostic to White Box Models
Decoding
(Longth Logical Form)
+
Our Method
89.5
Original Query Logical Form
Context Retrieval Time 0.6s
Yes
Yes
Table 5: Comparison of performance and efficiency between contrastive decoding and our method on the KG-FPQ dataset. *Average running time of each query on NVIDIA RTX A6000 GPU using Llama-3.1-8B Instruct model. Both methods require context retrieval.
do not require access to logits or internal model weights that operate exclusively at the input level, ensuring a fair comparison:
1) DirectAsk: Directly query the LLMs for an answer without additional processing or external retricval.
This approach relies on the model's internal knowledge and reasoning capabilities handle potential false premises.

---

Published in Transactions on Machine Learning Research (02/2020)
Original Query Stages
Logical Original Query
Logical Stages
FP%
74%
FP%
14%
FP%
14%
100
100
100
60
60
60
40
20
FN%
1% FN%
P% FN%
P%
F1 Score
Accuracy
F1 Score
Accuracy
F1 Score
Accuracy
Direct Claim
with RAG
G-retriever
GraphRAG/ToG
comparison of performance metrics across different retrieval methods using logical forms and/or original queries.
Figure 3: KG-FPQ dataset:
2) Prompt: We encourage the LLM to assess potential false premises before generating a response by appending the following prompt the original query: This question may contain a fasle premise. [query]
3) Majority Vote (MajVote): We prompt the LLM three times with the same prompt and select the most frequent response the final answer. This method improves reliability by reducing the impact of any single erroneous or hallucinated response. from LLM.
4) Perplexity A Utilizes a search engine to retrieve and incorporate real-time information from the web, enabling to provide answers based on the latest available web data. We use the version powered by GPT-4-Omni.
5) Direct RAG: Retrieves relevant entities from the knowledge graph and provides them context to the LLM alongside the original query. This approach augments the model's internal knowledge directly with external information to improve answer accuracy and grounding.
We report the performances of the following LLMs: GPT-4o-mini OOpenAl 2024), GPT-3.5-turbo OOpe nA 2023), -llama-.1--8--nsst Instruct (Qwen al. 2025 and Leren-1.5-7b-ooe (Bai et al., (023). To better understand where the performance gains come from, provide per-model breakdown of error corrections and premise-specific
(et al. 2024), Misttral-BB-Instruut--dll (Jiang et al. 2023), Qwen2.5-7ht
changes (false- vs. true-premise queries) in Appendix SA..1
Models
DirectAsk
Prompt
MajVote
DirectRAG
Ours
GPT-4o-mini
83.8
92.4
86.7
90.5
92.4
GPT-3.5
93.3
93.3
92.4
90.5
94.3
LLama-3.1
86.7
86.7
89.5
88.6
89.5
Mistral-7B
87.6
86.7
87.6
71.4
89.5
Qwen2.5
92.4
86.7
92.4
92.4
95.2
Qwen1.5
89.5
90.5
90.5
82.9
91.4
Perplexity AT
91.4
Table 6: Comparison of accuracy (%) of different hallucination miligation methods.
We evaluate question-answering accuracy on the hallucination mitigation task. Accuracy is caleu- lated by string matching the responses of LLMs: for TPQs, answering "Yes" is considered correct; for FPQs, answering "No" is considered correct.
Metrics.
Shttp://wwwwperppesttyy.
10

---

Published in Transactions on Machine Learning Research (02/2026)
5 Discussion
We show the result of the false premise detection task in Tab 2 8 and for the KG-FPQ. CREAK and
FEVER dataset, respectively. The SAC3 baseline result is shown in Appendix $ A.4.5 Tab. G presents the hallucination mitigation result.
Using logical forms helps better identify false premises in the qucstions. As shown in Tab. 2 for all three retrievers, explicitly incorporating logical forms into both retrieval and false premise detection stages significantly improves the identification of false premises. Sole reliance on original queries, even though potentially yielding high accuracy, tends to neglect accurate false premise identification, underscoring the importance of utilizing structured logical forms for tasks prioritizing precise false premise detection.
For the KG-FPQ dataset,, among different types of retrievers, when using logical forms in both the retrieval and false premise detection stages, the G-retriever method achieves the highest TPR at 94.44%, demonstrat- ing a strong capability in accurately identifying questions containing false premises. Notably, this method also achieves the highest F1 score (97.12%), indicating an optimal balance between precision and recall. Although the ToG method exhibits the highest TNR of 93.33%, significantly underperforms in TPR and overall Fl score (16.16%), suggesting limited effectiveness in correctly identifying false premises.
Notably, when original queries used in either retrieval, false premise detection, or both stages, despite achieving reasonable accuracy (73.33% and 79.69%), with RAG method shows significantly lower TPR (33.33% and 37.78%) compared to the first configuration. This suggests that relying on original queries alone, or in combination with logical forms in only one stage for detection, can achieve high accuracy due to correctly identifying negatives, it is less effective at capturing false premisos, which is the primary focus of our task.
Similarly, for the CREAK dataset, according to Tab. 3 using logical forms in both retrieval and detection stages consistently boosts performance versus operating on the original query. The gains are most pronounced for G-retriever: F1 score increases from 71.1% to 90.4% and accuracy from 74.5% to 90.5%, driven by a large increase in TPR (62.3% to 88.4%). GraphRAG/Tod Too already performs strongly with original queries, but still benefits from using logical forms, reaching the best overall scores (F1 92.1% and Acc 92.0%) with higher TPR (89.9% to 92.8%) while keeping FPR modest. The mixed configuration for retrieval mainly helps G-retriever but is less reliable than using logical forms in both stages, underscoring that structured queries plus explicit false premise detection are jointly necessary. Logical forms improves evidence targeting and makes contradictions salient to LLMs. Overall, the results indicate that logic-aware framework is crucial for converting strong retrieval into consistent end-to-end factuality.
According to Tab. the proposed design also yields consistent performance gains on natural language knowledge base, indicating that incorporating logical forms both the premise detection and LLM response stages remains effective when the knowledge source is less structured. This suggests that the benefits of logical-form guided reasoning stem from improved premise understanding and response control, rather than reliance a specific knowledge representation.
Explicitly detecting and informing LLMs false premise mitigates hallucination, as demonstrated in Tab. 0 Our proposed method, which directly communicates the presence of false premises to the models, achieves the highest accuracy: 92.4% with GPT-4o-mini, 94.3% with GPT-3.5, 95.2% with Qwen2.5, and 91.4% with 1.en-1.5. This performance surpasses alternative approaches such as Direct Ask, Prompl, Majority Vote, DirectRAG, and Perplerity AI.
Majority Vote does not perform well, likely due to hallucination snowballing, where repeated querying amplifies errors rather than correcting them. Additionally, while the Prompt method warns the model about potential false premises, it does not specifically tell the LLM which one contains false premises, negatively impacts performance on questions with valid premises, causes unnecessary cautiousness and reduces the model's ability to provide direct and confident answers. Besides, Perplexity Al does not perform as well potentially because the query format does not align well with graph data, leading to suboptimal retrieval of relevant information for certain types of questions. These findings emphasize the importance of tailoring hallucination mitigation strategies to both the model's reasoning process and the nature of the queries.
11

---

Published in Transactions on Machine Learning Research (02/2026)
Direct RAG retrieves and feeds raw evidence to LLMs without explicitly structuring the reasoning process highlighting inconsistencies between the query and retrieved facts. As a result, the model may surface relevant but semantically unaligned information, leading to shallow retrieval-based responses rather than true logical verification. In contrast, logical-form RAG + explicit false premise signaling forms a structured reasoning process: decomposes the claim into logical predicates and explicitly indicates when a premise conflicts with retrieved evidence. This guides LLMs to perform fact-level reasoning and contradiction handling, reducing overreliance on surface overlap and improving factual precision and interpretability.
Beyond hallucination mitigation, the proposed premise verification mechanism can be extended to sensitive or controversial topics, where unverified premises may amplify misinformation or harmful narratives. By explicitly detecting unsupported assumptions prior to response generation, the method offers a principled way to prevent models from uncritically engaging with inaccurate or inflammatory premises, enabling safer and more grounded interactions in high-risk domains. This suggests a broader role for retrieval-augmented logical reasoning as a lightweight safeguard for responsible deployment, especially in scenarios where factual grounding is essential before engaging in downstream reasoning dialogue.
95.3
95.2
79.4
79.3
Accuracy (1)
68
69.3
Mathed
Direct Clim
RAG
Gretriever
Single-hop
Multi-hop
Figure 4: GPT-40-mini and G-retriver: False premise detection accuracy across single-hop and multi-hop queries the KG-FPQ dataset. Using logical form-based RAG mainly helps detect false premises in multi- hop questions.
Our approach mostly improves false premise detection performance on multi-hop questions, according to Fig. 4 The incorporation of logical form-based RAG leads to notable performance gains compared to direct claim evaluation. Specifically, while single-hop questions moderate improvement, multi-hop questions benefit more, with false premise delection performance increasing from 68.7% in the direct claim setting to 79.3% with RAG and further to 95.2% when using the G-retriever. These results suggest that leveraging retricval mechanisms enhances reasoning over multiple pieces of evidence, reinforcing the importance of eetrieal-auggeent methods for complex question-answering tasks. We present a case study to illustrate how our method improves performance on multi-hop questions in Appendix $ A.4.6
5.1 Computational Cost Analysis
In Tab. we compare our method with the post-hoc Contrastive Decoding (Shi et al., 2023) approach terms of computational efficiency and model compatibility (accuracy result based on Llama-3. 1ama-..1-8B3). Our method reduces running time, uses fewer tokens by leveraging logical forms, and supports both model- agnostic and black-box settings. The proposed method introduces only a modest computational overhead, adding +)k+s) complexity to the retriever, where & is the number of retricved candidates and is the fcature size used for logical-form reasoning and premise detection. In practice, this addition remains lightweight since most of the cost lies the embedding model inference, which requires approximately 0.335 TFLOPs per example, while the remaining steps retrreval,, logical form conversion, and false premisee detection are implemented as efficient API calls. In contrast, post-hoc methods rely on fine-tuning and lack general applicability across different model architectures. We also include performance comparison of Contrastive Decoding with other LLMs in Appendix A.4.3
5.1.1 Significance Test
To ensure that the reported performance differences are statistically meaningful, we conduct paired t-tests across model configurations. The results, summarized in Table 7 and Table g confirm that the observed
12

---

Published in Transactions on Machine Learning Research (02/2026)
improvements are statistically significant (p < 0.05) in most comparisons. Table evaluates overall differences among performances of Direct. Claim, with RAG, and G-Retriever on the KG-FPQ dataset. Table g focuses on intra-method variations, contrasting setups that use logical forms in both stages (GG), only one stage (GO), or none (00). The results confirm that applying logical forms consistently across both retrieval and detection stages yields significantly higher performance, reinforcing the benefit of structured reasoning input.
Comparison
p-value
Comparison
p-value
G-Retriever wRAG
0.04
G-Retriever (GG) G- Retrieever (GO)
0.001
Direct Claim - G-Retriever
0.001
G- G-Rttrieeer (GG) - G- G-RRerieverr (00)
< 0.001
wRAG G- Getrriee
0.02
wRAG (GG) wRAG (GO)
0.01
Table 7: Statistical siguificance comparison across retrieval methods.
Table 8: Significance comparison of configurations within retrieval methods.
6 Conclusion
We propose a retrieval- anggeeee- logical reasoning framework that detects false premises to mitigate LLM hallucinations. Our method explicitly detects and signals false premises, overcoming key limitations of current approaches that rely on model parameters or post-hoc corrections. By incorporating upfront false premise detectioon, we prevent. hallucinations without requiring output generalion or model logil. access. Results show logical forms significantly improve false premise identification, especially for multi-hop reasoning questions. Our approach enhances LLM robustness by providing a structured mechanism to detect and handle misleading inputs before they influence rownst.ream responses. This reinforces the importance of structured reasoning techniques in improving model reliability.
Acknowledgments
This work was partially supported by the National Science Foundation under Award No. 2449280. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors also gratefully acknowledge support from the Amazon Research Awards and Capital One Research Awards.
References
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mci Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaolman Zhou, and Tianhang Zhu. Qwen technical report, 2023. URL attps://arxit org. 2atp.//39...111
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, llya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020. URL https: http::////xxww.o///a///////s//// 2005. 14165
Yihan Cao, Yanbin Kang, Chi Wang, and Lichao Sun. Instruction mining: Instruction data selection for tuning large language models, 2024. URL
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srini- vasan, Tianyi Zhou, Heng Huang, and Hongxia Jin. Alpagasus: Training a better alpaca with fewer data, 2024. URL htt.s:ttp///// wwwpeiivwr//abs////eee
13

---

Published in Transactions on Machine Learning Research (02/2026)
Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, and Pengcheng Hc. Dola: Decoding by contrasting layers improves factuality in large language models, 2024. URL https://arxivv.. org/ abs/ 2309. 03883
Shchzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. Chain-of-verificatio reduces hallucination in large language models, 2023. URL https://arxiv. org/abs/2309. 11495
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson From local to global: A graph rag approach to query-focused summarization, 2025. URL
Aaron Grattafiori et al. The llama herd of modcls, 2024. URL https://arxiv..rt http://w..rr//ab/222222222
Xisoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. G-retriever: Retrieval-auggmented generation ion for textual graph understanding and question answering, 2024. URL https://arxiv. wrtp:/ii..rrr/aab//2...0000 /402000000
Shengding Hu, Yifan Luo, Huadong Wang, Xingyi Cheng, Zhiyuan Liu, and Maosong Sun. Won't get fooled again: Answering questions with false premises, 2023. URL httpp://arxiv... org.vvo..g//aaae http//abb22230..222
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2): 1 55, January 2025. ISSN 1558-2868. doi: 10.1145/3703155 URL/http://// doi oto/ 1rg/100.1145/37035555
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaune Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b, 2023. URL attps://arxis httpa/////axxvooo///a/////// arg/abs//310.... 06825
Najoung Kim, Ellie Pavlick, Bureu Karagol Ayan, and Deepak Ranachandran. Which linguist invented the lightbulb? presupposition verification for question-ansswring, 2021. URL http::://aaxxvv.rrrr/ abe/ 2101.
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale Fung, Mohammad Shoeybi, and Bryan Catanzaro. Factuality enhanced language models for open-ended text generation, 2023. URL https: /tatpii//arxiv..o org/
0ba/2200.0466
Li Li, Chenwei Wang, You Qin, Wei Ji, and Renjie Liang. Biased-predicate annotation identification via unbiased visual predicate representation. In Proceedings of the 31st ACM International Conference on Mul- timedia, MM '23, pp. 1410-4420, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400701085.. doi:
URL https://doi wwrpiiiorggg1..10 wttp.o...1.//3/811//////1 36118477
Li Li, Wei Ji, Yiming Wu, Mengze Li, You Qin, Lina Wei, and Roger Zimmermann. Panoptic scene graph generation with ceinunttics-roooyyyye learning. Proceedings of the AAAI Conference on Artificial Intelli- gence, 38 3844):1145 111111555311,,, Mar. 2024. doi: 10.1609/aai 2a1609/aaai.vaa v384..2384 URL https://ojs. htt.///sssaaaaa..aaaa/.aaaaaaa.a org/index. hhtp/AAt//rtticl//veee///
Li Li, Peilin Cai, Ryan A. Rossi, Franck Dernoncourt, Branislav Kveton, Junda Wu, Tong Yu, Linxin Song, Tiankai Yang, Yuehan Qin, Nesreen K. Ahmed, Samyadeep Basu, Subhojyoti Mukherjee, Ruiyi Zhang, Zhengmian Hu, Bo Ni, Yuxiao Zhou, Zichao Wang, Yue Huang, Yu Wang, Xiangliang Zhang, Philip S. Yu, Xiyang Hu, and Yue Zhao. A personalized conversational benchmark: Towards simulating personalized conversations, 2025a. URL ftttps//arrxv..oo wwtp///////...11/////// 14106
Shawn Li, Peilin Cai, Yuxiao Zhou, Zhiyu Ni, Renjic Liang, You Qin, Yi Nian, Zhengzhong Tu, Xiyang Hu, and Yue Zhao. Secure on-device video ood detection without backpropagation, In International Conference on Computer Vision (ICCV), October 2025b.
14

---

Published in Transactions on Machine Learning Research (02/2026)
Shawn Li, Huixian Gong, Hao Dong, Tiankai Yang, Zhengzhong Tu, and Yue Zhao. Dpu: Dynamic prototype updating for multimodal out-of-distribution detection. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10193 10202, June 2025c.
Shawn Li, Chenxiao Yu, Zhiyu Ni, Hao Li, Charith Peris, Chaowei Xiao, and Yue Zhao. Defenses against prompt attacks learn surface heuristics, 2026, URL https://arxiy. wwtp://///a/....000005511..
Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, and Jundong Li. Knowlodge graph-onhancod large language models via path selection, 2024a. URL httpss//arrii..rrr//b/4444...
Ollic Liu, Deqing Fu, Dani Yogatama, and Willie Neiswanger. Dellma: Decision making under uncertainty with large language models, 2024b. URL
Ziyi Liu, Soumya Sanyal, Isabelle Lee, Yongkang Du Rahul Gupta, Yang Liu, and Jieyu Zhao. Self- contradictory reasoning evaluation and delection, 2024c. URL https://arxxv..rrg/ wwwp://wwwwwrgrb/////////aasa/ 096003
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models, 2023. URL
Costas Mavromatis and George Karypis. Gnn-rag: Graph neural retrieval for large language model reasoning, 2024. htttt::ttt/araaae org/ ab20//05. 20139
Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, and Roger Levy. Line: A neurosymbolic approach for logical reasoning by combining language models with first-order logic provers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1153-5176, Association for Computational Linguistics, 2023. doi: 10.18653/v1/2023
emnlp-main.313. URL http:://x.....// http:::/xx..oo.orrg...... o0./ 18653/v1/22222 emnlp-main.33 .13
Yasumasa Onoe, Michael Q. Zhang, Eunsol Choi, and Greg Durrett. Creak: A dataset for commonsense reasoning over entity knowledge, 2021. URL. httpss:/aarss..orr//bb///1.....
OpenAl. Gpt-3.5-turbo Large language model, 2023. URL http:::/pppaaaaooooopopooee wtt.://cccccccoccacae gpt-3-5 Accessed: 2024-03-18.
OpenAl. Gpt-4 technical report, 2024. URL http:://arai..org/b//22000.0717
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. Med-halt: Medical domain halluci- nation test for large language models, 2023. URL http:://aaraiv..rr//b//0001/ 15343
Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang. Logic-Im: Empowering large language
models with symbolic solvers for faithful logical reasoning, 2023a. URL httppa::///xxivoor/aab////////
12295
Liangming Pan, Xinyuan Lu, Min-Yer Kan, and Preslav Nakov. Qacheck: A demonstration system for question-guiet multi-hop fact-checking, 2023b. URL https://arxiv, wwwp:/or//aaa...oo.../
Pouya Pezeshkpour. Measuring and modifying factual knowledge in large language models, 2023. URL
https://arxiz http:::://axivvor///bbbaa. hrrg/abs/2306 202a//333000664
Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yugiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https:://// //t::i/////vveve wwaexiii...///.. abs 21112.111111
Ansh Radhakrishnan, Karina Nguyen, Anna Chen, Carol Chen, Carson Denison, Danny Hernandez, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilè Lukošiūtě, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Sam McCandlish, Sheer El Showk, Tamera Lanham, Tim Maxwell, Venkatesa Chandrasekaran, Zac Haddield-Dodds,, Jared Kaplan, Jan Brauner, Samuel R. Bowman, and Ethan Perez.
15

---

Published in Transactions on Machine Learning Research (02/2026)
Question decomposition improves the faithfulness of model-generatedd reasoning, 2023. URL https://
www.://gg//aaaaaa.......o... 11768
Li Shawn, Jiashu Qu, Linxin Song, Yuxiao Zhou, Yuehan Qin, Tiankai Yang, and Yue Zhao. Treble counter- factual VLMs: causal approach to hallucination. In Association for Computational Linguisties: EMNLP 2025, pp. 14423-18434, Suzhou, China, November 2025. Association for Computational Linguistics. ISBN 979-8-89116--33-7
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Scott Wen tan Yih. Trusting your evidence: Hallucinate less with context-aware decoding, 2023. URL https:///aaaa http:/aaaivv../aaaaaaeee wwtg/bg//////eeeeeee a305
14739
Ben Snyder, Marius Moisescu, and Muhammad Bilal Zafar. On carly detection of hallucinations in factual question answering. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and New York, NY, USA, 2024a. Association for Computing Machin- URL https://doi org/10. atrg/0..114//663777 1145/3637528.
Data Mining, KDD '24, pp. cry. ISBN 9798400704901. doi: 10. 00.114//6637558.36777778
3671796
Ben Snyder, Marius Moisescu, and Muhammad Bilal Zafar. On early detection of hallucinations in factual question answering. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2721 2732, 2024b.
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhno Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung- Yeung Shum, and Jian Guo. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph, 2024. URL
James Thorne, Andreas Vlachos, Christos Christodooulopoulss,, and Arpit Mittal. FEVER: a large-scale dataset for fact extraction and VERification. In Marilyn Walker, Heng Ji, and Amanda Stent (eds.), Proceedings of the 2018 Conference of the North Americon Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 509-819, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 1..18633vv1N11/ 1074. URL https://acllnthhl..om http://daaattiooooooorrrr///ooaa
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Nikolay Bash- lykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Fer- rer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylor, Pushkar Mishra, Igor Molybog, Yixin Nic, Andrew Poulton, Jeremy Reizenstein, Rashi Rungla, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subra- manian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Ilyyan Zarov, Yuchen Zhang, Angela Fan, Mclanic Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Llama 2: Open foundation and fine-tuned chat models, 2023. URL htttt:::////attt .org/ /200s//330..22 2307.09288
Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. stitch in time saves ninc: Detocting and mitigating hallucinations of 11ms by validating low-confidence generation, 2023. URL
attps://arxiv. org//baa. 2307. 03987
Tu Vu, Mohit Tyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoe Te, and Thang Luong. Freshllms: Refreshing large language models with search engine augmentation, 2023. URL https://arxiv httt::///arriiooorr/bb2/211
Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Brian Ichtor, Fei Xia, Ed Chi, Quoe Lc, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2023. URL
https://arxiv wwtpoo/////...111//eep
httgss//axiv.orrr/a////....111 11903
16

---

Published in Transactions on Machine Learning Research (02/2026)
Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, and Quoe V. Le. Simple synthetic data reduces sycophancy in large language models, 2024. URL https://axxii.....ma org/ abtp///s.////... 03958
Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu. Faithful logical reasoning via symbolic chain-of-thought, 2024. URL https://arxiv..rrg/ hwwpr///b//240.... 18357
Nan Xu and Xuezhe Ma. Decoprompt: Decoding prompts reduces hallucinations when large language models meet false premises, 2025. URL
http://bb///11.07//// 07457
Xinyan Velocity Yu, Sewon Min, Luke Zettlemoyer, and Hannanch Hajishirzi. Crepe: Open-domain question answering with false presuppositions, 2022. URL
Hongbang Yuan, Pengfei Cao, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, and Jun Zhao. Whispers that shake foundations: Analyzing and mitigating false premise hallucinations in large language models, 2024. URL https://aaxix: www./bgg///////o/oo//../...eeeee 2402. 19103
Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A. Malin, and Sricharan Kumar. Sac3: Reliable hallu- cination detection in black-box language models via semantic-aware cross-check consistency, 2024. URL https://arxiv.ot org/ abs// /tbb//33111 221. 01740
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. How language model hallucinations can snowball, 2023a. URL https://arxiv http:///aaxivv.og////.sss// 2205. 13534
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. Siren's 's song in the ai ocean: survey on hallucination in large language models, 2023b. URL https://arrxt www. 2309.
01219
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and lon Stoica. Judging llm-as- a-judge with mt-beneh and chatbot arena, 2023. URL httts:///aaaat: http///xxvvo.//aaaaa///// www.oogg////./ooo/////..ot..ee/t /twp//30........ 05685
Liwen Zheng, Chaozhuo Li, Xi Zhang, Yu-Ming Shang, Feiran Huang, and Haoran Jia. Evidence retrieval is almost all you need for fact verification. In Tun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Findings of the Association for Computational Linguistics: ACL 2024, pp. 9274 9281, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10.18653 /v11 2024 findings-acl.551. URL atttps//aclanttool.e org/2024. f findings-acl.661/
Yanxu Zhu, Jinlin Xiao, Yuhang Wang, and Jitao Sang. Kg-lpq: Evaluating factuality hallucination in 1lms with knowledge graph-based false premise questions, 2024. URL httph::/ttas:t: wttprxiiwrrggr.////o/ooooooaaaer /
17

---

Published in Transactions on Machine Learning Research (02/2026)
A Appendix
A.1 KG-FPQ Dataset Details
In KoPL (Zhu et al, 2024 each entity is linked to a specific concept, such as Leonardo da Vinci being connected to the concept of an artist. The knowledge graph includes 794 distinct concepts, categorized into domains based on general knowledge, enabling domain-basee entity classification. For the art domain, the au- thors of (Zhu et al., 2024) manually selected 33 relations, cnsuring that each relation is relevant to its domain and informative, avoiding ambiguily. For example, the relation arhist is linked to the Art domain, while family is more ambiguous and excluded. Table the representative concepts, relations and subjects in the art domain of KG-FPQ. The dataset comprises 4969 questions in the discriminative task for the art domain, with each true premise question modified using the following editing methods: SoighborSaame Canne-Coccept (NSC), Neighboo-Differennt-Cnnee (NDC), Nol-Neighboo-Samee-CC Concept (NNSC), Not-Neighhor-DDe Seaee--ieeeee-oooeeee (NNDC), Not-NeighhborSaat Saaeoor--ae----aae Relatonn (NNSR), and Not-Neighbor- Different-Relatee (NNDR).
Domain Concept e.g.
Concept Qty Subject e.g.
Subject Qty
Relation e.g.
Relation Qty
film television series 44
Titanic Moderu Family Hamlet
cast member composer narrative location
Art
1754
33
drama
Table 9: Representative concepts, relations, and subjects in KG-FPQ art domain.
A.2 CREAK Dataset Details
The CREAK dataset Onoe et al. (2021) designed test. whether language models can combine factual knowledge about specific entities with commonsense reasoning. It consists of 13k English claims covering 2.7k entities, each labeled true false. These claims require reasoning that bridges factual information (e.g. "Harry Polier is wizard") with unstated commonsense inferences (e-g., "If someone is good at skill, they can teach it"). Unlike prior commonsense benchmarks that focus generic physical or social scenarios. CREAK emphasizes entity-grounded reasoning and assesses whether models can verify claims that depend on both knowledge retrieval and implicit reasoning. Table 10 summarizes the dataset statistics.
Split
# Claims
Average Length
# Unique Entities
Vocab Size
Total
True
Falsc
(# tokens)
Train
10,176
5,088
5,088
10.8
2,096
19,006
Dev
1,371
691
680
9.7
531
4,520
Test
1,371
707
664
9.9
538
4,620
Test (Contrast))
500
250
250
10.0
226
1,596
Table 10: Data statistics of CREAK.
A.3 Prompt Details
The following prompt is used to combine the information retrieved from the knowledge graph G (context) and the query logical form L(q) (query) to form the input the LLMs discussed in the Section False Premise Detection with Logical Form.
Given the context below, does the following question contain false premise? Answer with 'Yes' or 'No" only. Note that the context is provided as valid facts in triple. Context: [context]. Query: [query].
We use the following prompt for logical form conversion:
You are given question. The task to: 1) define the predicates used the question. 2) parse the question into logic rules based on the defined predicates 3) translate any logical rules implied by the question. 4) convert the question into
18

---

Published in Transactions on Machine Learning Research (02/2026)
logical form using predicate logic. Provide your final answer in the following
format: Logical form: Predicatel (entityl, entity2). Keep all expressions concise and consistent. Use standard predicate logic notation.
A.4 Additional Results
A.4.1
Query-level Hallucination Mitigation Analysis
Model
Extra Correct
FPQ Improved
TPQ Change
GP-T-4omint
427
392
+35
GPT-335
50
60
-10
Llama-3.1
139
115
+24
Mistral-7B
94
108
-14
Qwen2.5
139
124
+15
Qwen1.5
74
+22
Table 11: Premise-level breakdown of hallucination mitigation improvements. Extra Correct counts queries newly answered correctly compared to direct prompting. FPQ Improved counts false-premise queries cor- rected after false-premise detection. TPQ Change indicates net changes on true-premise queries after the detection and informing process.
A.4.2 Logical Form Ablation Study
Full
w/o Rel.
w/o Ent,
w/o Ent,
Logical Form for Retrieval Original Query for Detection
TPR 0.377
0.044
0.067
0.011
TNR
0.866
0.000
0.067
0.267
FPR
0,133
1.000
0.933
0.733
FNR
0,622
0.956
0.933
0.989
F1
0.540 0.073
0.109
0.020
Acc
0.800
0.038
0.067
0.048
Logical Form for Both Stages
TPR
0.600
0.044
0.067
0.044
TNR
0.866
0,000
0.133
0.267
FPR
0.133
1.000
0.867
0.733
FNR
0.400
0.956
0.933
0.956
F1
0.740
0.073
0.109
0.076
Acc 0.829
0.038
0.076
0.076
Table 12: Logical form ablation results using with RAG.
We examine the contribution of individual components in the logical form, such entity arguments and relational structure, by analyzing how the removal of specific elements affects false-premise detection across different retrieval backends. We consider variants where the logical form is used for retrieval while the original query is used for false-premise detection, as well as variants where the logical form is applied to both stages. The results are shown in Tab. 12
13 14 Across all retrieval backends, removing relational structuro or entity arguments from the logical form consistently degrades performance. This suggests that the logical form is most effective when its constituent elements are jointly preserved, and that each component contributes complementary information for identifying unsupported premises.
A.4.3 Comparison with Post-hoc Method
Tab. 18 presents a performance comparison between Contrastive Decoding Shi et al. 2023 a post-hoc hallucination mitigation method, and other LLMs (Mistral-7B, Qwen1.5, 1-ren22257B-Instruee Llama-3.1- 8B- Instrucct.. Our method achieves improved performance over Contrastive Decoding on all models except Mistral-7B.
19

---

Published in Transactions on Machine Learning Research (02/2026)
Full
w/o Rel.
w/o Ent
w/o Enta
Logical
Form for Retrieval
Original Query for Detection
TPR
0.822
0.178
0.078
0.111
TNR
0.933
0.133
0.067
0.133
FPR
0.066
0.867
0.933
0.867
FNR
0.177
0.822
0.922
0.889
F1
0.869
0.270
0.124
0.177
Acc
0.838
0.171
0.076
0.114
Logical Form for Both Stages
TPR 0.944
0.167
0.089
0.133
TNR
0.991
0.133
0.133
0.200
FPR 0.009
0.867
0.867
0.800
FNR 0.056
0.833
0.911
0.867
F1 0.971
0.254
0.144
0.211
Acc
0.952
0.162
0.095
0. 143
Table 13: Logical form ablation results using G-retriever.
Full
w/o Rel.
w/o Ent
w/o Ent
Logical
Form
Retrieval
Original Query for Detection
TPR
0.089
0.056
0.089
0.078
TNR
0.933
0.133
0.200
0.267
FPR
0.067
0.867
0.800
0.733
FNR
0.911
0.944
0.911
0.922
0.162
0.093
0.145
0.130
Acc
0.813
0.067
0.105
0.105
Logical Form
Both Stages
TPR
0.089
0.100
0.100
0.067
TNR
0.933
0.133
0.200
0.267
FPR 0.067
0.867
0.800
0.733
FNR
0.911
0.900
0.900
0.933
0.162
0.163
0.162
0.112
Acc
0.813
0.104
0.114
0.095
Table 14: Logical form ablation results using GraphRAG/Ton
A.4.4 False Premise Detection
We additionally evaluate Llama-3.1-8b, as well as GPT-3.5-turbe and G-retriever on the false premise detec- tion task using method. The results are presented below (Tab. 15 and Tab. 17 Fig. 3). Notably, when original queries are used in either retrieval, false premise detection, accuracy (91.11%), G-retriever shows markedly lower TPR (37.78%) compared to the first configuration. This suggests that relying on original queries alone, or in combination with logical forms in only one stage for detection, can achieve high accuracy due to correctly identifying negatives, is less effective at capturing false premises, which the primary focus of our task.
both stages, despite achieving high
A.4.5 Premise Detection Baseline
We include SAC3 Zhang al. (2024) baseline for premise detection for the KG-FPQ dataset. Our proposed approach achieves better performance when considering both F1 score and Accuracy (sce Table for comparison).
2
A.4.6 Case Study
We perform a case study demonstrating how our method enhances multi-hop question performance. We use the with RAG method example here, where the edited object not directly connected to the subject within the KoPL knowledge graph.
20

---

Published in Transactions on Machine Learning Research (02/2026)
Metric
Direct Claim
G-retriever Original Query for Both Stages
with RAG
GraphRAG/ToG
TPR
0.878
0.811
0.644
0.800
TNR
0.200
0.467
0.467
0.267
FPR
0,800
0.533
0,533
0.733
FNR
0.122
0.189
0.356
0.200
F1
0.873
0.854
0.744
0.832
Accuracy
0.781
0.762
0.619
0.724
Logical Form for Retrieval and Original Query for False Premise Detection
TPR
0.878
0.811
0.711
0.800
TNR
0.200
0.400
0.333
0.267
FPR
0.800
0.600
0.667
0.733
FNR
0.122
0.189
0.289
0.200
F1
0.873
0.849
0.780
0.832
Accuracy
0.781
0.752
0.657
0.724
Logical Form for Both Stages
TPR
0.878
0.800
0.811
0.800
TNR
0.200
0.867
0.400
0.267
FPR
0.800
0.133
0.600
0.733
FNR
0.122
0.200
0.189
0.200
F1 Accuracy 0.781
0,873
0.878
0.849
0,832
0.810
0.752
0.724
Table 15: KG-FPQ dataset: comparison performance metrics across different retrieval methods using original queries and logical forms at different stages using Llama-3.1-8b.
Method
TPR
TNR
FPR.
FNR
F1
Acc
SAC3
81.1
73.3
26.7
18.9
87.4
80.0
Table 16: Performance of SAC3 on premise detection KG-FPQ dataset.
Question: Is "The Dark Knight" the recipient of the 16th Screen Actors Guild Awards?
Yes, "The Dark Knight" won the award for Outstanding Performance by a Stunt Ensemble in a Motion Picture at the 16th Screen Actors Guild Awards.
Answer:
Logical Form:
recipient of ('The Dark Knight', 16th Screen Actors Guild Awards)
Retrieved Graph Triple: ['The Dark Knight', 'award received', '81st Academy Awards']
After Detecting and Informing LLM of the Presence of a False Premise:
Corrected Answer:
No, "The Dark Knight" was not the recipient of the 16th Screen Actors Guild Awards. That year's SAG Award for Outstanding Performance by a Cast in a Motion Picture went Inglourious Basterds, not The Dark Knight.
A.5 Evaluation under Longer Queries
Longer questions often introduce additional contextual cues, compositional structures, and distracting de- tails, which can obscure the core false assumption and make premise verification more challenging, Evaluating under such settings is important for understanding whether model can robustly identify and reason about incorrect premises rather than relying on surface-level patterns.
As an initial step, we select the first 105 questions from the KG-FPQ dataset and use an LLM (gpt-4o- mini) to rewrite them into longer, more natural user queries while preserving the original (true or false) premises. We then evaluate our method on both false-premise detection and hallucination mitigation under this longer-context setting. The prompt used for query rewriting, along with the rewritten queries, is released
21

---

Published in Transactions on Machine Learning Research (02/2026)
Metric
G-retriever
Original Query for Both Stages
True Positives (TP%)
37.78
True Negatives (TN%)
100.00
False Positives (FP%)
0.00
False Negatives (FN%)
62.22
F1 Score (%)
54.84
Accuracy (%)
91.11
Logical Form + Original Query
True Positives (TP%)
37.78
True Negatives (TN%)
100.00
False Positives (FP%)
0.00
Falsc Negatives (FN%)
62.22
F1 Score (%)
54.84
Accuracy (%)
91.11
Logical Form for Both Stages
True Posilives (TP%)
75.56
True Negatives (TN%)
80.00
False Positives (FP%)
20.00
False Negatives (FN%)
24.44
F1 Score (%)
84.47
Accuracy (%)
79.37
Table 17: False Premise Detection Performance using GPT-3.5-turbo and G-retriever.
Original Query for Both Stages
Logical Form + Original Query
Logical Form for Both Stages
FP%
TN%
FP%
FP%
TN%
100
80
80
80
40
20
FN%
5%
FN%
T% FN%
T%%
Score
Accuracy
F1 Score
Accuracy
F1 Score
Accuracy
Direct Claim
with RAG
G-retriever
GraphRAG/ToG
Figure 5: Additional comparison of performance metrics across different retrieval methods using logical forms and/or original queries.
in accompanying repository? The False Premise Detection result and Hallucination Mitigation result are shown in Tab. 19 and Tab. 20 Overall, the results on this longer query subset show that the proposed method consistently improves performances on both false-premise detection and halluciaation mitigation tasks, suggesting that its effectiveness extends beyond short minimally phrased queries.
22

---

Published in Transactions on Machine Learning Research (02/2026)
Mistral-7B
Qwen1.5
Qwen2.5-7B-Instruee
Llama-8.1-8B-Instrue
Contrastive Decoding
89.5
76.2
85.7
84.8
Ours
87.6
89.5
92.4
86.7
Table 18: Comparison between contrastive decoding and our method across different LLMs. Note: GPT-3.5 and GPT-4o-mini are not included as logits not available for contrastive decoding approach.
Metric
Direct Claim
with RAG G-retriever Original Query for Both Stages
GraphRAG/ToG.
TPR
2.22
85.56
68.89
92.22
TNR
50.00
93.33
73.33
53.33
FPR
50.00
6.67
26.67
46.67
FNR
97.78
14.44
31.11
7.78
F1
4.04
91.67
79.49
92.22
Accuracy
8.65
86.67
69.52
86.67
Logical Form for Retrieuol and Original Query for Detection
TPR
2.22
94.44
93.33
92.22
TNR
50.00
73.33
53.33
53.33
FPR
50.00
26.67
46.67
46.67
FNR
97.78
5.56
6.67
7.78
F1
4.04
94.97
92.82
92.22
Accuracy
8.65
91.43
87.62
86.67
Logical Form for Both Stages
TPR
2.22
94.44
97.78
92.22
TNR
50.00
73.33
16.67
53.33
FPR
50.00
26.67
53.33
46.67
FNR
97.78
5.56
2.22
7.78
F1
4.04
94.97
94.62
92.22
Accuracy
8.65
91.43
90.48
86.67
Table 19: False-premise detection performance under longer queries.
A.6 Logical Form Correctness Validation
Our current human evaluation was conducted by two Ph.D. students with relevant NLP/LLM rescarch experience. All annotators reached full agreement, resulting in inter-annotator agreement of 1.0.
A.7
Additional Experiment Setup
All models are implemented and run on a multi-NVIDIA RTX 6000 Ada workstation. For Logical Form Extraction and Retrieval, we set parameters temperature and top_p= 1.
23

---

Published in Transactions on Machine Learning Research (02/2026)
Setting
A Acuracy
Direct Ask
0.933
Ours
0.952
Table 20: Hallucination mitigation accuracy under longer queries using GPT-4o-mini.
24


Reviews:
[{"review_id": "j7vUbPrzm0", "reviewer": "Reviewer_w3Nx", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper deal with the problem of false premises, which means means that when the users of llm input some questions, there are some factual error in their questions or  incorrect implications in their statements. Previous methods deal with this problems via facual alignment training or constraint decoding, while this paper proposed a new method of using RAG methods to first find verify whether there is a false premise in the question, then incorporate the verification results into the decoding process.\n\nStrength 1: This paper is different from the previous methods. This paper focus on prevention instead of cure. According to the paper, this can reduce the error/hallucination accumulated throughour the reasoning and decoding process.\n\nStrength 2: This paper induce logical form to deal with the problem, which is new in the context of solving false premise.\n\nStrength 3: This paper introduce a method that is efficient and model agnostic. Which means this method if very fast versus constractive decoding, pass at k genenration method.\n\n\nHowever, there are also some weaknesses about this paper regarding to the problem scope and dataset used. \n\nWeakness 1: The largest concern is about whether the proposed method can be generalized. The paper only evaluated on single sentence questions on KG-FPQ and CREAK, and \n\nWeakness 2: the mitigation method is also very simple, just to add a sentence when there is a false premise detected. In some more realistic situation, the user may have much longer input with several sentences or several paragraphs. Then the evaluation scope cannot cover this senario, and also the mitigation of adding a single sentence saying \"there are false premises\" may fail.", "claims_and_evidence_yn": "Yes", "claims_explanation": "There are three major claims:\n\nClaim 1. Logfical forms enhance retrieval and detection. Yes, this is well supported in the experiments of KG-FPQ (Table 2 and 3)\n\nClaim 2. Proactive Detection and help reduce hallucination. Yes, this is also well supported arroding to Table 5. \n\nClaim 3. The propose method is fast and compatible with diffferent models as black boxes. Yes this claim is also veirified according to extenssive experiments.", "audience_yn": "Yes", "audience_explanation": "This paper deal with an important problem that may produce llm hallucination, and many people are working on solving this problem. I believe the auduence of TMLR is interested in this paper.", "requested_changes": "1. Add the session title \"introduction\" of the first session. The current version makes \"related work\" as the first session, which is weird. \n\n2. It is strongly suggested to find more datasets, or create/synthesis a dataset that contains longer user questions with false premises, and evaluate your methods on this setting to show whether your method can work on longer context.", "broader_impact_concerns": "I think there are no broader impact concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9y4hnCiPVV", "reviewer": "Reviewer_DGvS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a logical-form–guided verification framework that prevents LLM hallucinations caused by false premises. The method converts a query into a logical form, checks its factual consistency via KG-based retrieval, and explicitly signals false premises to the LLM before generating an answer. Experiments on KG-FPQ and CREAK show substantial improvements in false-premise detection and reduced hallucinations across multiple LLMs, without requiring model logits or fine-tuning. \n\n**Strengths**\n\n(S1) Clear and timely motivation\n\n(S2) Coherent methodology design\n\n**Weaknesses**\n\n(W1) Lack of validation for logical-form correctness\n\n(W2) Dependence on KG quality\n\n(W3) Lack of query-level analysis for hallucination mitigation", "claims_and_evidence_yn": "No", "claims_explanation": "(W1) Lack of validation for logical-form correctness: The framework crucially depends on the accuracy of the generated logical forms, yet the paper provides only a limited human evaluation. The assessment covers just 200 samples and uses a coarse three-point scale that cannot capture semantic or structural nuances essential for reliable premise verification. Moreover, the annotators’ expertise and independence are not described, raising concerns about potential conflicts of interest and the credibility of the reported perfect scores. The paper also fails to analyze how logical-form errors propagate through retrieval and verification. Given that many user queries do not cleanly map to predicate–argument structures, the lack of rigorous validation undermines confidence in the robustness of the approach.\n\n(W2) Dependence on KG quality: False-premise detection fully relies on evidence retrieved from a knowledge graph, yet the paper does not discuss KG construction, coverage, or noise characteristics. Real-world KGs frequently contain incomplete or incorrect triples, and the proposed system has no mechanism for detecting or mitigating KG-induced errors. This omission leaves the method vulnerable to incorrect premise classification and suggests the need for robustness analysis under KG perturbations or missing evidence.\n\n(W3) Lack of query-level analysis for hallucination mitigation: The hallucination mitigation evaluation reports only overall accuracy improvements. To substantiate the core claim that selective prompting improves model behavior specifically when false premises are present, the authors must show that the queries benefiting from improved accuracy correspond to FP-detected instances. Without such query-level correlation analysis, the causal link between false-premise detection and hallucination reduction remains unverified.", "audience_yn": "Yes", "audience_explanation": "(S1) Clear and timely motivation: False-premise hallucination remains an under-addressed yet practically important failure mode of LLMs, and the paper provides a clear and compelling motivation for intervening before answer generation.\n\n(S2) Coherent methodology design: The pipeline (i.e., logical-form extraction, KG-based verification, and selective prompting) is conceptually aligned and model-agnostic, making it broadly applicable to black-box LLMs.", "requested_changes": "(W1) Lack of validation for logical-form correctness: Provide a substantially more rigorous evaluation of logical-form generation, including larger-scale assessment, annotator qualifications, and analysis of error propagation through retrieval and verification.\n\n(W2) Dependence on KG quality: Discuss the construction and reliability of the underlying KGs and include robustness experiments (e.g., missing or noisy triples) to demonstrate that premise detection is not overly fragile to KG imperfections.\n\n(W3) Lack of query-level analysis for hallucination mitigation: Report per-query or subset-level results showing whether accuracy improvements occur specifically on false-premise-detected queries to justify the claimed causal effect of selective prompting.", "broader_impact_concerns": "Although the proposed method primarily aims to improve LLM safety by reducing false-premise hallucinations, several broader impact concerns should be noted. Because the framework relies heavily on knowledge graphs, any inaccuracies or biases in the KG may lead the system to misclassify valid user queries or reinforce incorrect facts as authoritative evidence. \n\nAdditionally, the mechanism could potentially be repurposed for content filtering if false-premise detection is applied to sensitive or controversial topics. These issues merit discussion in a Discussion section.", "additional_comments": "Please refer to the comments above.", "extra_scores": {}, "extra_text": {}}, {"review_id": "XXWzNIXiJq", "reviewer": "Reviewer_chN4", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a retrieval-augmented logical reasoning framework for detecting and mitigating hallucinations in LLMs caused by false premises in user queries. The key idea is to prevent hallucinations proactively rather than addressing them post-hoc. The approach has three main components: (1) converting user queries into logical form representations (predicate-argument structures), (2) using these logical forms with various retrievers (embedding-based, non-parametric G-retriever, LLM-based GraphRAG/ToG) to verify premises against a knowledge graph, and (3) explicitly informing the LLM when a false premise is detected before generation.\n\n**Strengths:**\n- The proactive approach to hallucination mitigation (detecting false premises before generation) is well-motivated and practical, avoiding the need for model logits or fine-tuning\n- The use of logical forms for structured retrieval is intuitive and shows consistent improvements across different retriever types\n- Comprehensive evaluation across multiple retrievers and LLMs, with ablation studies on single-hop vs. multi-hop questions\n- The method is model-agnostic and compatible with black-box LLMs, making it practically deployable\nStatistical significance tests are provided (Tables 6-7)\n\n**Weaknesses:**\n- Evaluation is limited to two datasets, both primarily focused on entity-relation verification in structured knowledge graphs\n- The logical form extraction relies on GPT-4o-mini, creating a dependency on a capable LLM for the preprocessing step\n- Limited analysis of failure cases and edge cases where logical form extraction may fail", "claims_and_evidence_yn": "Yes", "claims_explanation": "- Logical forms improve false premise detection: Tables 2-3 clearly demonstrate consistent improvements when using logical forms in both retrieval and detection stages across multiple retrievers. The G-retriever achieves 94.44% TPR with logical forms vs. 33.33% without on KG-FPQ.\n- The method mitigates hallucinations: Table 5 shows the proposed method achieves the highest accuracy across most LLMs compared to baselines (DirectAsk, Prompt, MajVote, DirectRAG, Perplexity AI).\n- Multi-hop questions benefit more: Figure 4 supports this claim, showing larger gains for multi-hop (68.7% → 95.2%) vs. single-hop questions.", "audience_yn": "Yes", "audience_explanation": "The paper addresses an important and timely problem in LLM reliability. False premise-induced hallucinations are a practical concern in deployed systems, particularly in high-stakes domains like healthcare and finance (as the paper motivates with the penicillin example). The finding that logical form representations improve retrieval-based premise verification across multiple retriever architectures provides actionable insights for practitioners building RAG systems.\n\nThe work should interest researchers working on: Hallucination detection and mitigation, Retrieval-augmented generation, Knowledge graph-based reasoning, LLM reliability and factuality", "requested_changes": "Critical for acceptance:\n\nBroader evaluation: The current evaluation is limited to KG-FPQ and CREAK, both of which involve structured knowledge graph verification. Please evaluate on at least one additional dataset that tests false premise detection in less structured settings (e.g., open-domain QA with natural language evidence).\n\nRecommended improvements:\n- Ablation on logical form components: It would be informative to ablate which aspects of logical forms (entity extraction vs. relation extraction vs. structured format) contribute most to the improvements.\n- Generalization to other LLMs for logical form extraction: Currently, only GPT-4o-mini is used. Testing with open-source alternatives (e.g., Llama) would demonstrate broader applicability.", "broader_impact_concerns": "No significant ethical concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)