Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation
Authors: Tobias Leemann
Periklis Petridis
Giuseppe Vietri
Dionysis Manousakas
Aaron Roth
Sergul Aydore
Venue: ICLR 2025 Poster (2025)
Keywords: domain adaptation; NLI; RAG; document-grounded; NLP;
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We present an automatic strategy to adapt NLI models for grounding verification to challenging inputs encountered in realistic RAG systems using synthetic data.

Abstract: While retrieval-augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or irrelevant information. A common detection strategy involves prompting the LLM again to assess whether its response is grounded in the retrieved evidence, but this approach is costly. Alternatively, lightweight natural language inference (NLI) models for efficient grounding verification can be used at inference time. While existing pre-trained NLI models offer potential solutions, their performance remains subpar compared to larger models on realistic RAG inputs. RAG inputs are more complex than most datasets used for training NLI models and have characteristics specific to the underlying knowledge base, requiring adaptation of the NLI models to a specific target domain. Additionally, the lack of labeled instances in the target domain makes supervised domain adaptation, e.g., through fine-tuning, infeasible. To address these challenges, we introduce Automatic Generative Domain Adaptation (Auto-GDA). Our framework enables unsupervised domain adaptation through synthetic data generation.
Unlike previous methods that rely on handcrafted filtering and augmentation strategies, Auto-GDA employs an iterative process to continuously improve the quality of generated samples using weak labels from less efficient teacher models and discrete optimization to select the most promising augmented samples. Experimental results demonstrate the effectiveness of our approach, with models fine-tuned on synthetic data using Auto-GDA often surpassing the performance of the teacher model and reaching the performance level of LLMs at 10% of their computational cost.

Published as a conference paper at ICLR 2025
AUTO-GDA: AUTOMATIC DOMAIN ADAPTATION FOR GROUNDING VERIFICATION IN RETRIEVAL- AUGMENTED GENERATION
Tobias Leemann"
Periklis Petridis" MIT
Giuseppe Vietri AWS Al Labs
University of Tübingen
tobias. wwwmanneunn--eeeeeeee.....e www.ununnnntttttinnn....ne
periklis@mit.cm edu
#ietzigyvamazzn.com com
Dionysis Manousakas AWS AI Labs di wmw.anaamzonnn com
Aaron Roth AWS AI Labs
Sergül Aydöre AWS Al Labs
aarenrct www.conrcttmmaaznccmom
saydoree amazon. com
ABSTRACT
While retrieval-augmenned generation (RAG) has been shown enhance factual- ity of large language model (LLM) outputs, LLMs still suffer from hallucination, generating incorrect or irrelevant information. common detection strategy in- volves prompting the LLM again to assess whether its response is grounded in the retrieved evidence, but this approach costly. Alternatively, lightweight nat- ural language inference (NLI) models for efficient grounding verification can be used at inference time. While existing pre-traines NLI models offer potential so- lutions, their performance remains subpar compared to larger models on realistic RAG inputs. RAG inputs are more complex than most datasets used for training NLI models and have characteristics specific to the underlying knowledge base, requiring adaptation of the NLI models to a specific target domain. Additionally, the lack of labeled instances in the target domain makes supervised domain adap- tation, e.g., through fine-tuning, infeasible. To address these challenges, we intro- duce Automatic Generative Domain Adaptation (Auto-GDA). Our framework en- ables unsupervised domain adaptation through synthetic data generation. Unlike previous methods that rely on handcrafted filtering and augmentation strategies, Auto-GDA employs an iterative process to continuously improve the quality of generated samples using weak labels from less efficient teacher models and dis- crete optimization to select the most promising augmented samples. Experimental results demonstrate the effectiveness of our approach, with models fine-tuned on synthetic data using Auto-GDA surpassing the performance of the teacher model and reaching the performance level LLMs at 10 % of their computational cost.
INTRODUCTION
Large Language Models (LLMs) are increasingly used in consequential applications. Despite their versatility, LLMs often produce hallucinations, in which the generated information is inaccurate or fabricated and require costly retraining integrate
knowledge. One promising method to mitigate these problems retrieval-aue -ariivallaugmeeteed generation (RAG, et al. al. (020). RAG enhances text generation by adding information from external knowledge sources the prompt and has been shown to reduce ballucinations in practice (Shuster et al. 2021). Nevertheless, even when modern
LLMs are used with RAG, hallucination rates of 15% 30% (Chen al.) 2023a) more than one hallucination per 100 output tokens can occur (Nuu et al. 2024).
To prevent hallucinated output from being delivered end-users, natural language inference (NLI) models can be used to verify the grounding of the generated output in the documents retrieved (Chen)
2023b Es al. 2024 Tang et al. 2024) before the output relayed the end-user: the
Work done during internship AWS AT Labs.

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generated response must be fully grounded in the documents, i... it must be logically inferrable from the
10x
LLMs: egg. GPT-4, Claude-3.5
documents; otherwise, is considered ungrounded. However, we need to check the outputs at inference time, we require lightweight NLI models with very low latency. The current landscape of available NLI models for verifying grounding in RAG is illustrated in Figure [ based on results obtained in our evaluation of correctness and inference time (see Table 3 for full numeric results): Some recent works such as Mini-Check (Tang et al. 2024)
inference time
Pre/Post-
2.5x
Processing:
c.g. AlignScore
lightweight: e.g., DeBERTa, Mini-Check
domain-ndapted, lightweight
+20% performance
model
0.72
S: 0.88
grounding verification performance (RAG data, ROC-AUC)
Figure 1: Landscape of current grounding verifica- tion models for RAG. While LLMs have the best per- formance, they incur about 10x higher latency than lightweight models. In this work, we are interested in ob- taining lightweight models with LLM-level performance for grounding verification through domain adaptation.
have developed lightweight models for NLI, e.g.. based on RoBERTa (Liu 2019) These models have shown good performance on academic benchmarks. However, our results indicate that their performance in verifying grounding for realistic RAG inputs lags behind L.LMs by about 20% (in ROC-AUC scores). Other recent methods use pre- and post-pro post-processesing techniques such sentence tokenization or LLM prompting to decompose long prompts (Zha et al. 2023 Es et al. 2024) into several chunks or facts. Each of these chunks needs to be processed in separate forward pass, resulting in high latency well. While some studies (e.g., |Maakuul et al., 2023: Tang et al., 2024) have also explored directly using LLMs like GPT-4 for text entailment detection, their latency is about order of magnitude above the lightweight models. Taken together, these characteristics make it hard to deploy the existing approaches in real-time industry use-cases.
The performance gap observed for realistic RAG inputs with the lightweight models may point to a substantial domain mismatch between the NLI datasets used to train these models and the challeng- ing, real-world data encountered at test time. We observe that inputs of NLI models in RAG are more challenging they comprise longer segments with multiple statements and contain more subtle un- grounded information as the output is LLM- --M-eenerated. While these characteristics are common to RAG systems general, each implementation still has very individual input distribution: First, inputs may follow a specific format due to the RAG prompt template e.g., question: <question>> evidence: Pussage <evidence Passage 2 <evidence2> .... Second, the documents are retrieved from knowledge bases from variety of different domains, which may not be represented in training data. Prior work (Williams et al., (018)) confirms difficulties when NLI models are applied to data from an unseen domain and Hosseini et al. 2024) shows generalization gap of up to 20%. This suggests that NLI models need to be adapted to their target domain for optimal performance.
Bridging this domain gap poses significant challenge due to the inherent difficulty of adapting models to unseen domains that is further amplified by the prohibitive costs of obtaining labeled data from the target domain. This prevents supervised domain adaptation, e.g., through target domain data. To address this issue, we propose Automatic Generative Domain Adaptation (Auto-GDA), Our unsupervised domain adaptation framework produces high-quality synthetic data, which is then used to fine-tune a lightweight NLI model, adapting it to a specific domain of RAG inputs. While training data generation by simply prompting L.LMs has been repeatedly explored in the literature (e.g.. Saad-Falcon et al.) 2024, Hosseini et al.) 2024). data quality might be fur- ther improved through filtering and incorporating background knowledge through presering data augmentation strategies, such round-trip translation (Chen et al.) (023b). However, speci- fying good filters and heuristic augmentation strategies require significant manual effort. As data augmentations can further be applied iteratively, the space of potential samples grows exponentially, necessitating efficient search strategies. During this offline training phase, less efficient teacher mod- els can provide additional guidance using weak labels. Auto-GDA offers unified way leverage all these available tools. We thus make the following contributions:
on
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1. We formalize the unsupervised domain adaptation problem under the availability of practi- cal tools such as data generators, data augmentation routines, and weak teacher models.
2. We propose Automatic Generative Domain Adaptation (Auto-GDA), a principled frame- work for unsupervised domain adaptation through synthetic data that can be instantiated with different implementations of generation, augmentation, and weak labeling steps and which automatically selects high-quality samples.
3. We show that our objective corresponds to an enhanced distribution matching objective but is highly efficient optimize. w
4. Our experiments on realistic RAG inputs highlight that our fine-tuned models using Auto- GDA (1) often outperform their weak teacher models (2) perform almost as well as ref- erence models fine-tuned with human-labeled data and (3) reach the level of performance exhibited by LLMs while having almost 10x lower latency. (4) Our method further outper- forms more classical training-basse based unsupervised domain adaptation techniques
2 RELATED WORK
The problem of domain adaptation is concerned with adapting existing models to different domains. We introduce the most closely related approaches in this section and refer the reader to Ramponi & Plank (2020) for further references.
Synthetic NLI Data. Related works explore synthetic data generation for NLI models. Hosseini itt al. (2024) generate the diverse, cross-domain GNLI (General NLI) dataset synthetically in two steps: first prompting an LLM to generate target domains, then using a prompt-tuned LLM to gen- erate training statements. [Tang et al. (2024) generate synthetic training data for their MiniCheck models using document-to-claim generation and claim-to-document generation. We compare to their model in our experimental section and show that it can be further improved through domain adaptation. (Saad-Falcon et al. (2024) use synthetic data to specifically improve RAG system eval- uation. They generate synthetic in-domain data with few-shot prompt. However, their method is compared within RAG evaluation frameworks and not tested for NLI performance.
Synthetic Data for Domain Adaptation in NLP. While synthetic QA data generation is well- explored (Shakeri et al. 2020: Ushio et al.) 2022 Yue et al. 2022] Lee et al. 2023). synthetic data for NLI domain adaptation has received less attention, potentially due to the difficulty of generating
realistic and difficult samples. Wang et al. 2023a) propose an iterative synthetic data generation
scheme requiring partially labeled data. They generate initial seed data using an LLM prompt that is iteratively refined based on errors from a model trained on lamaan- reference set. Unlike this work, we assume very limited access labeled data from the target domain.
Classical Unsupervised Domain Adaptation. Beyond synthetic data approaches, classical unsu- pervised domain adaptation (UDA) techniques have also been applied in NLP. These include tech- niques to align embeddings (Chen et al. [2018] Li et al.] [0088] [houudhy et al. [202) (unn & Saenko) 2016), pre- pre-training approaches (Gururangan al. 22200] Han & Eisenstein (019) or virtual adver- sarial training (Miyato et al.] 2019 Jiang et al.) 2020). See Appendix for details.
We borrow the term "teacher model" from the knowledge distillation literature (Gou et al. 2021 Yang et al., 2020). However, our problem differs from distillation problems because our target dataset is unlabeled. In this paper, we focus on the problem of systematically generating and select- ing the most beneficial synthetic samples that can be created through initial generation and iterative augmentation steps. We do so using an efficient objective that can be interpreted as a form of distri- bution matching.
PRELIMINARIES
Domain adaptation is concerned with adapting ML model pretrained source domain make predictions on a target domain when the underlying data distributions differ across the two domains. The unsupervised domain adaptation problem further complicated due to the lack of labeled data in the target domain. While features are available, there is no direct information about the correct
Code aonilable at pet //glt twp... www..bboo.. con /wwzzon aci encce oBBA- wientt wwwent-rrourreeee -round ing-Ver- Vern Filst con- BAG
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class labels for the target domain samples. This poses significant challenge the model must learn to adapt to the new distribution without explicit guidance.
3.1 UNSUPERVISED DOMAIN ADAPTATION FOR NLII
Data Domains. Following the common natural language inference (NLI) setup, assume data iid. from a source domain is available as a set of triples D. = {((e,, , Cs. Br. Bi) In=........ ~ Ps containing evidence E X, corresponding claims E X' where X' denotes a space of text sequences, and labels y V. This data used to train an initial model f : X X + y. We use D, to denote sets of samples and Ps to denote the data density of the source distribution. Note that in a RAG use case, the evidence e will contain the user prompt as well as the retrieved documents. Additionally, we are from the target domain. They
provided with a set of J unlabeled samples D. = are sampled from Pt, the data distribution faced at test time (e.g., the realistic RAG inputs). Our goal is to adapt model pretrained on Ps to perform well on Pt- In this work, we are focusing on problems where J is small. This scarcity makes it challenging to accurately estimate the underlying distribution of the target domain, which can hinder the effectiveness of traditional domain adaptation methods that rely
substantial amount of target data. We study the binary NLI task where Y E {0. 1]. A positive label (y=1) is only assigned all information in the claim can be inferred directly from the evidence; claims that are contradictory to the evidence cannot be inferred from the evidence are considered non-entailed (y=0).
In this work, we focus on covariate shift between the two domains: While the prior p(e, c) is subject to change across domains, the true relation between specific features and labels, p(yle, c) is consistent for the source and the target domain. For the NLI task considered here, this assumption is sensible because the entailment relation itself does not change for different domains. Following prior work (Saad-Falcon et al. 2024)). we slightly deviate from the fully unsupervised setup by supposing that very small portion of the target domain can be manually labeled and used as validation set for hyperparameter tuning only, as commonly done in ILI literature (Laban et al.) 2022; Tang et al.) Zha et al. 2023). We show that our method works with validation sets as small as 30 samples.
2022
We extend this common setup to incorporate three additional tools that are readily available in practice: First, we have powerful generative LLMs that we can use to generate new samples based on the unlabeled examples using techniques such as prompt-tuning (Lester et al.) 2021), or few-shot prompting. The generator G can be formally described as (randomized) function G: x x (2))² x - X, meaning that G takes input a piece of evidence and set of F > example claims (e.g., for few-shot prompting) and a desired target label. The generator G is then tasked with producing a new claim sample that reflects the style of the provided claims and has the specified target label. Note that we provide the F claims without known label, so they can either be entailed or con-entailed w.r.t. e. Second, we can use some background knowledge of the task to define some approximately label-preserving augmeniution strategies to increase diversity, e.g., us- ing paraphrasing models, round-trip translation or synonym replacements (Chen et al. 2023b). This step can be formalized as mutation function M X' - X' which takes claim ifies it while trying to preserve its label. The label- aabel-preserving characteristics of these strategics are imperfect, i.e., with a small probability the entailment relation will be affected by the augmentation. Finally, we suppose a teacher model T X' x X - [0, 1]. which can be applied to the data from the source and the target domain and provides entailment score. The teacher model performs reliably within the source domain, but only provides a weak estimate of T(e c). The performance of this model may be noisy because the target domain is out-of-domain for this model, and the model may be too inefficient to be deployed in practice. We will use this model to obtain weak estimates of the samples' labels. We now present our framework Auto-GDA, which incorporates the three tools G, M, T named above in a principled algorithm.
Helper Tools.
input and mod-
4 A PRINCIPLED FRAMEWORK FOR UNSUPERVISED DOMAIN ADAPTATION
4.1 OUTLINE OF THE FRAMEWORK
In this work, we present Auto-GDA GDA, a framework for Automatic Generative Domain Adaptation, that generates synthetic data points that are useful for fine-tuning pretrained model f for the target
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Initial data generation using generator teacher model T
final high-quality data
Unlabeled Target Data D.
Labeled Synthetic D
fine-tune model f
Select best
Apply aug- mentations using M,T
samples minimize Lue.
Repeat 1, times
Augmented
Augmented Synthetic Data D(i+1)
Synthetic Data +
Quality Scores
Compute sample contributios Cua
Figure 2: Overview of Auto-GDA. We generate initial data using the generator G, which signed entailment certainty scores using teacher model T. The synthetic data is iteratively aug- mented using M, whereas labee-preservation is confirmed with T and entailment certainties updated. We finally select the top- K samples that minimize an objective function Lua- These steps can be applied iteratively until the final data is used fine-tune the model for the target domain.
domain. For the data generation process to result in high fine-tuning utility it must meet several criteria: (1) The data must be realistic and non-trivial, (2) must have high diversity, (3) the assigned labels must be of high quality.
Auto-GDA is specifically designed to tackle these three challenges. As RAG outputs stem from LLMss, we also generate realistic initial claim samples for given evidence using LLMs. We lever- age few-shot prompting to transfer patterns the output to the generated samples. To preserve the diversity of the evidence (which contains the relevant documents in the knowledge base), we gen- crate synthetic claims sequentially for each unique piece of evidence e available the unlabeled target dataset D,. This has the advantage that the broad diversity of documents in the knowledge base is represented. We propose to apply augmentations on the synthetic data to increase diversity further. As the augmentation strategies are only approximately label-preserving, we have to keep track of increasing label uncertainty to detect samples with low-quality labels when several data augmentation steps are applied. We therefore equip each sample with an entailment certainty score an estimate of the probability of the sample having an entailed label (y=1) which can be used to remove samples with low-quality labels. Auto-GDA applies these steps iteratively to successively increase data quality. In summary our framework consists of the following steps, which we describe more detail in the next sections:
1. Initial Generation. Generate an initial sample population D. {(Cx, ( ( of claims è and labels ý for the evidence e using the generator G. Use the teacher model T to assign initial entailment certainty (0) scores to each sample of synthetic data. This results in each sample having a hard label ğ and a "soft" confidence score y())) for the hard label.
2. Sample Augmentation. Apply augmentations M on claims in the population D to obtain claims with the same hard labels. Update their entailment certainties using the teacher model again. Merge mutated samples and samples from previous iteration to form updated population D (1+1) [(c, Dr, ri )) that is of larger size L > K.
3. Sample Selection. Select the subset of samples of size K from (++11) that minimize our proposed enhanced distribution matching objective Lia formally introduced in Eqn. (4) The objective includes the unlabeled target samples De and the certainty scores. The selected subset becomes the next generation dataset (i+1)
4. Repeat steps 2 and 3 for fixed number of iterations until objective Lun converges.
We illustrate these steps in Figure 2 and will detail out implementation choices for each step below.
4.2 GENERATING REALISTIC INITIAL DATA
LLMs have been repeatedly used to generate synthetic data for various domains, including NLI Saad-FFlloo et al. 2024 Hosseini al. 2024). In this work, we generate initial data using few- shot prompting with the prompts provided in Appendix E.] The prompt instructs the LLM to generate synthetic claims G(e, claim 5) for the evidence e, reflecting the style of ex-
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ample claims from De (claim(Dt,e) denoting claims from target data for the evidence e) and target label y E {0,1}. 1). For label y = 1, the LLM is instructed to include only grounded facts, for ğ = 0, some ungrounded information should be introduced. We assign labels y according to the prompt used, resulting in complete initial generated tuples (ć, ū). We follow some related works (Puri et al.) 2020 Vu al. 202T), which have suggested generating many samples and only keeping the most confident. To do so, the samples can be equipped with a weak estimate of the label probability using the teacher model, e.g., another LLM or an NLI model with sophisticated pre- and postprocessing. In the binary classification setup, we can compute initial entailment certainties r( T(e, é), which can be interpreded as an uncalibrated and potentially noisy estimate of p(y l/e, é). We explore L.MSs for data generation and use state-of-the-ar NLJ models and also LLMS teacher models T for providing initial entailment certainties. Adding the entailment certainty scores y(0) to the respective tuples we obtain set of triples D = {(Ckk Ur,
" ))
K after this step.
4.3 INCREASING DIVERSITY THROUGH LABEL- PRESERVING DATA AUGMENTATIONS
In this section, we demonstrate how to augment the initial synthetic dataset (generated using the few-shot prompt- ing strategy) for additional diversity, while maintaining high degree of label certainty for the augmented synthetic data points. We exploit certain de- gree of background knowledge to de- rive data augmentation strategies (Chen) et al.] 2023b). For instance, we know that paraphrasing the claims while pre- serving their semantic meaning should not change their entailment label. How- ever, when iteratively applying para- phrasing operations, we have to account for increasing probability of acciden- tally flipping the label.
e: Paris is the capital of France, a country in Europe. Paris has 2.1 million inhabitants.
Paris, capital of France, has 2.1 million inhabitants. y: I (ontailment)
catuils c centalls
entails e'
Paris is capital of France, a country Europe. Paris has 2.1 million inhabitants.
el: There is a capital in Europe with 2.1 million inhab- itants.
Jr (entuilment)
rewritten sample
Figure 3: Intuition for our update rule for entailment certainties: If parent claim e is entailed by e and mutated claim ex is entailed by its parent c, the mutated claim è' will be entailed by e as well.
Obtaining High-Quality Entailment Certainties. We can combine the generative models with discriminative teacher models again to obtain weak estimates (() of the entailment certainty of the augmented samples. Instead of directly computing the entailment probability using T, we exploit logical invariances, which allow for better estimates depicted in Figure 3 If the original claim is en- tailed by the evidence, and if the modified claim is entailed by the original claim, the modified claim will also be entailed by the evidence. Suppose we have obtained é' = (() as modification of the synthetic claim è. As we already have an estimate of the entailment probability for (e, é). we can reuse it and only need to compute another entailment probability for (é, ē¹). We argue that comput- ing this entailment probability is easier for the teacher model than directly computing T T(e, é'), as the claim and the modified claim should be semantically and syntactically more similar. Paraphrasing datasets like PAWS (Zhang et al. (2019) are common pretraining datasets, and standard NLI datasets like MNLI (Williams et al., (018) contain many similar samples due their construction through edits, so II. predictionss are expected to be more reliable on these pairs. Querying the teacher model on T é²) allows us to use the following update rule for the augmented sample (c, č'):
(1) using the entailment certainty ,(( of the original tuple (e, as a base. Some teacher models may be particularly reliable with claim-claim pairs rather than with evidence-claim pairs, so it can be useful to choose a different teacher model for this update than for obtaining initial certainty scores.
((tt)) é') (0) (e, é) T(è, é') + (1-r(((e,,) e)) (1 (1-T T(C,
Label Invariant Augmentation Strategies: In this work, we consider three augmentation strategies that will likely preserve entailment labels (see Appendix C.T for additional details):
Partial Rephrasing with LLMs. Our first augmentation is an LLM--ased rephrasing step. Specifically, we randomly mask 20% of the words of the input sequence by replacing the corresponding words by " and ask an LLM (Anthropic's 's Claude3 Haiku) to impute the gaps while preserving the meaning.
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Complete Paraphrasing, We use T5-based paraphrasing model (Vorobev & Kuznetsov 2023). We generate paraphrases for the claims enforcing diversity using a constraint that prevents n-grams of length greater than 5 from being regenerated.
Sentence Deletion. We chunk the claim into sentences and randomly delete one of them.
This should preserve the entailment relation as it only removes information. However, note that this augmentation may remove some of the context necessary to understand the entire claim.
We generate several augmentations for each sample using these strategies along with an estimate of their entailment probabilities, resulting in an enlarged sample set. Unfortunately, not all of these samples may of high quality. Therefore, crucial to select only the most promising samples.
4.4 AUTOMATIC SELECTION OF HIGII-QUALITY SAMPLES
A key component of our work involves automatically selecting the most promising samples. Intu- itively, we are interested in finding samples that resemble target data. This includes both having realistic features and correctly assigned labels. The data should also have high chance of improv-
ing the final model. Provided with an augmented dataset D {c, Út, ri) at iteration i, we are interested in selecting subset Qe D (1) of smaller size |Qel = K that only contains the most promising samples. We propose the following objective function to assign a loss to a selected subset Qe which contains three terms for each selected sample:
Ltot =
Σ
d(c,, Cmin. )
AaL 1i)
A ((
és G.....
distance
label correctness
utility torm
where d(x,x') [|{(()) (x)' is distance function over inputs in X' defined via textual em- beddings 1), Cmin,i == arg min colaim(D.. d(c', (.) the closest claim for evidence e from the
target dataset, and Ad, A, are hyperparameters. LDiv [0. 1] x fo, 1} + R4 is a function that penalizes uncertain labels taking the certainty scores and the hard labels ý as inputs as plotted in Figure 4 We derive the exact form of the LDiv function as a divergence estimate of the condi- tional distributions in Appendix B.2 The distance term encourages samples be close claims from the target data set for the given evidence. The la- bel correctness term penalizes samples where the en- tailment certainties are too far apart from the target
0
labels and is used to discourage selection of samples
label corr. tern @
9=1
where the labels are likely to be incorrect. Addition- ally, we encourage generation of samples where the pretrained model f is not performing well yet by in- cluding the cross-entropy loss of the model as a util- ity term, y CE é), ø] where is the assigned hard label a synthetic sample.
2-
Notably, Equation (2) can be derived from first principles as an enhanced distribu- tion matching objective. By defining parametric distri- butions Po.e(c. y) (representing the selected synthetic data for evidence e) and (C,, y) (representing the target distribution for e we aim to imitate) the objec- tive corresponds to the divergence between these dis- tributions minus the expected utility of the synthetic data. Formally.
Theoretical Properties.
0.00
0.25
0.50
0.75
1.00
entailment certuinty p!0
Figure 4: Modeling the label correctness term in Eqn. 2las aas function of When the estimated entailment certainty r does not match the assigned hard label ý this term takes high values, discouraging selection.
(ior(Re, DEL (pQ,e(c; (c. (Poo.ee (c, y)) E (Uy(c. v)]
(3)
We derive a proposition to formalize this connection in Appendix B
Optimizing the objective. Optimizing the objective for a subset Qe containing K synthetic samples for evidence e with minimal loss can be done highly efficiently in three steps: (1) Computing each samples' contribution to the sum in Liots (2) ranking the samples by this contribution, and (3) greedily selecting the top-K K subset of samples with the lowest contributions. Pseudocode of our complete framework is provided in Algorithm (Appendix).

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Dataset
RAGTruth
LFQA-Verif. QA
SummEdits Summary
Avg., Rank
RAG-Task
Summary
QA
Aua-GDA complex misatnos Hox mnnddd
FLAN-TS
0.734
0.708
0.655
0.700
0.699
BART-large
0.696
0,670
0.821
0.769
0.739
DeBERTav2
0.782
0,530
0.645
0.876
0.708
DAPT
DAPTDBBER DeBERTaV2
0.746
0.00
0.703
0016
0.813
now
0.837
0.775
DiFTDa2
0.785
1 0.008
0.566
1: 0,08
0.880
0030
0.845
0.001
0.769
CORAL
0.718
# 0.001
0.677
+ 0.001
0.822
0.001
0.853
0.001
0.768
MiniCheck-T5
0.754
0.640
0.741
0.791
0.732
AlignScore
0.729
0.822
0.904
0.894
0.837
Vectara-2.1
0.805
0.854
0.648
0.590
0.725
Flan-T5 (Auto-GDA)
0.756
+ 0:044
0.783
+: 0.013
0.687
+ 0,022
0.824
0.762
BART (Auto-GDA)
0.813
= 0.009
0.867
= 0.001
0.867
# 0.066
0.860
0,010
0.852
DeBERTav2 (Auto-GDA)
0.837
+ 0.007
0.867
* 0.007
0,925
0.000
0.883
0.00s
0.878
GPT-3.5
0.706
0.648
0.749
0.814
0.729
GPT-4o-mini
0.884
0.833
0.812
0.878
0.852
GPT-4o
0.892
0.865
0.896
0.880
0.883
Table Performance comparison to baselines (ROC scores). Grouped by off-the-shelf base mod- els trained on standard data, domain-adapted versions of the best base models using DAPT, SIFT, and DeepCORAL, complex state-of-the-art models trained using custom datasets (Vectara, MiniCheck) or using postprocessing (AlignScore), proprictary LLMs, and versions of the base models fine-tuned with Auto-GDA. We highlight the teacher model that was used to assign initial label certainties r (())
in box and make three observations: (1) the Auto-GDA version of the base models always im- proves over the vanilla versions and the versions trained with SIFT, Deep CORAL, and DAPT, (2) our best-performing model DeBERTa V2 (Auto-GDA) outperforms its teacher model three out of four cases, and (3) BART and DeBERTa with Auto-GDA reach LLM-level performance.
5
EXPERIMENTAL EVALUATION
We run experiments with realistic datasets and baseline models to confirm the efficacy of Auto-GDA.
Datasets. We evaluate our approach on three datasets for document-groumde question answering (QA). We select datasets that include documents, realistic LLM-generated long- form answers, and human labels that can be used for testing. The SummEdits dataset (Laban et al.) 2023) contains GPT-3.5-gemerated and manual summaries of documents from different domains, e.g., judicial, sales emails, podcasts. We further use both the summary and the QA portion of the RAGTruth dataset (Niu al., 2024). The RAGTruth dataset contains summaries and answers to questions created by LLMs (GPT-3.5/4, Mistral, Llama2). Finally, we use the L.FAA-Veriiiattio dataset (Chen al., (2023)), which retrieved documents for questions from the "Explain me Like I am five" -datsstt and generated corresponding long-form answers with GPT-3.5 and Alpaca. We selected the datasets to cover characteristies of realistic RAG systems including specific prompt templates (present in RAGTruth,, LFQA- Ver--ffiicaaiin)) and various domains (present in all datasets, specifically in SummEdits). Details and links to the datasets can be found in Appendix
summarization and
Base models. As NLJ models, we use three pretrained model architectures that are able to handle IL. queries with the longer context required for RAG inputs. We investigate a BART-Large (Lewis) et al. 2019) model pretrained only on the MNLI dataset (Williams et al.) (018). This can be consid- ered a lightly pretrained model. Additionally, we study DeBERTa-V2 pretrained with datasets from the TaskSource collection (Sileo 2024). We additionally study FLAN-T5-based model (Raffel et al. 2020 pretrained on MNLI. The all models possess context lengths at least 1024 tokens.
Baselines. We use state-of-the-ard baselines: AlignScore (Zha et al. 2023) (RoBERTa-bascd with pre- and postprocessing), MiniCheck (Tang et al. 2024). and Vectara-2.. (both T5-based). These complex state-of-the-ar models are trained using custom datasets (Vectara, MiniCheck) or use post- processing (AlignScore). As a teacher model to assign initial score, we use the best- persormefo model from the "complex" category, which (unlike LLMs) allow easy access to uncertainty scores
httpps:// wect cow/docs
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Dataset RAG-Task
RAGTruth
LFQA-Verif. SummEdits Mean (Gap closed)
Summary
QA
QA
Summary
non-fine-tuned
0.782
0.530
0.645
0.876
0.708 (0%)
+ft. on Few-Shot Data
0.799
0.826
0.934
0.872
0.858 (84%)
+It. on Augmented w/ Random Selection
0.777
0.783
0,919
0.862
0.835 (71%)
+ft. on Augmented w/ Objective (Auto-GDA)
0.837
0.867
0.925
0.883
0.878 (96%)
fine-tuned on labeled
0.842
0.890
0.909
0.898
0.885 (100%)
Table 2: Ablation: Fine-tuning with synthetic data obtained by few-shot prompting and random selection of augmentations as opposed to using our framework Auto-GDA. We also report perfor- mance relative the hypothetical upper baseline of fine-tuning labeled target data and observe that we can almost close this domain-adaptation gap (ROC, DeBERTa model, avg. over 5 runs).
and have acceptable performance. We employ opt a a principled way of choosing the remain- ing hyperparameters X,, Ad, and the other teacher model used to estimate entailment probabilities for augmentations in Eqn. We perform 50 trials per dataset and use the ROC score of a fine-tuned DeBERTaV2 model on the small validation dataset as selection objective. In case limited budget for hyperparameter tuning available, we recomment setting X,= [20, 50) which led sta- ble performance. Auto-GDA is run for two iterations on RAG and one iteration on the other datasets, generating synthetic datasets between 1.3 x and 2x the original dataset size. We found no improvements through further increasing dataset size. We also compare against several common UDA methods, including robustness-baad based approaches. Specifically, we implement DAPT (Guru- rangan al. 2020), SiFT (He et al. 2020), and Deep-CORAL (Sun & Saenko 2016) for further pretraining of the DeBERTa-V2 V2 model.
5.1 SYNTHETIC DATA FOR NLI MODEL FINE-TUNING
We present the main results obtained with Auto-GDA in Table Our results show that Auto-GDA is highly effective and improves performance in ROC-AUC scores of all tested models on all datasets.
Comparison to Teacher Models. Auto-GDA is highly effective, not only incorporating the knowl- edge of the stronger teacher model (indicated by box) but often even surpassing it, as the optimiza- tion enhances data quality over the teacher in three out of four datasets.
Comparison to Classical UDA Methods. Traditional UDA methods (DAPT, SiFT, and Deep- CORAL) did not yield significant improvements in our NLI domain adaptation setting and Auto- GDA consistently outperforms them across all datasets. This also indicates that synthetic data gen- eration is more effective for NLJ tasks.
Comparison to LLMs. Finally, our fine-tuned models reach performance levels between state-of- the-art LLMS such as GPT-4o and GPT4o-mini while maintaining significantly lower computational requirements. This shows that our approach results in models with superior NLI performance, in particular when compared to the non-fine-tuned or non-LLM baselines.
Other Teacher Models. We investigate using I. L.MSs and other teacher models in Table 2 (Apeendix) but observe that LLMs do not generally outperform other teacher models, possibly due to unreliable uncertainty scores. However, the table also shows that the DeBERTa model can improve its own performance through self self-supervision by an average of 0.15 AUC when applied as the teacher model.
5.2 ABLATION INVESTIGATIONS
Components of the algorithm. We add the components of our algorithm individually and show how they successively increase performance in Table 2 In all ablations, we keep dataset size and other parameters constant. The biggest gain is achieved by fine-tuning on data created through few- shot prompting. We subsequently add data augmentations without applying our selection criterion, but instead selecting fhw-shot and augmented samples randomly. We observe that this decreases data quality, highlighting that data augmentation is only beneficial together with our filtering criterion. When we do and apply data augmentation with our filtering step (corresponding to
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Model
RAGTruth
LFQA- Verificiii
SummBdits Summary
Inference time (relative)
Performance
Summary
QA
QA
sec/(50 samples)
AUC-ROC+100
Vectara
1.57 +: 0.02
1.13 0.03
1.35 + 0.03
1.03 0.01
1.27 (59%)
72.5
FLAN-TS
0.07
0.07
1.72 = 0.07
1.71 0.07
1.71 (80%)
69.9
DeBERTaV2
2.56 0.03
1.88 0.04
2.15 +: 0.06
1.88 0.09
2.12 (100%)
70.8
MiniCheck-TS
4.50
3.06
0.14
3.22
3.69 (174%)
73.2
BART-large
4.33 0.01
3.62 + 0.06
3.95 + 0.09
3.76 0.20
3,92 (184%)
73,9
AlignScore
5.88 0.12
7.55
+:
7.55 0.35
1.81 0.06
5.70 (269%)
83.7
GPT-4o
19.80 0.51 19.11
0.44
21.09 1: 2.97
21.89 1: 1.26
20.47 (967%)
88.3
Auto-GDA Detenavz
Same as DeBERTav2
2.12 (100%)
87.8
Table 3: Inference times of the models on the datasets as well es average performance taken from
Table
Our DeBERTa model combines LLM-level performance with substantially lower latency.
full Auto-GDA), this increases performance overall with one exception on the I.FQA-Veificcati dataset (note however that performance here is already above the labeled data, so selection based on larget data may draw the results toward the labeled data scores as well). As upper baseline interested in the hypothetical performance reachable by fine-tuning on human-labeled samples and include it in Table Considering the difference between the fine-tuning models and the models fine-tuned on human-labeled data as the domain adaptation gap, expressing our results relative to these baselines indicates that we manage to close an impressive 96% of this gap.
5.3 INFERENCE EFFICIENCY
Linking to our motivational Figure we study the efficiency of our models in Table 3 We compute NLI scores for 50 random samples from the respective datasets. We observe models three cate- gories: The most efficient models (Vectara to BART-large) have medium performance on the RAG datasets used in this work indicated by their ROC. On the other hand, models using sophisticated post-processing (AlignScore) perform better, but require about 2.5 times more inference time than our most successful DeBERTa model. Finally, LLMs via APIs require about 10-fold inference time, but result in highest performance. When we compare models trained with our approach, we observe LLM-level performance at about 10% of the inference time. Although our primary focus lies on inference, we report generation times for Auto-GDA in Table 16 (Appendix) for completeness.
6 DISUSSION AND CONCLUSION
In this work, we show that synthetic data can successfully tackle the domain generalization gap for NLI models. We present Auto-GDA, an automatic approach for synthetic sample generation and selection overcoming the need for tedious heuristic or manual filtering and augmentation selection. Our results show that we can obtain models that perform on par with most powerful LLMs while having around 90% less inference time using method. By generating synthetic data, we can provide a more comprehensive and tailored representation. allowing for greater control over the de- sired features. Our results also confirm the common intuition that generalization increasingly hard with smaller models (Bhargava et al., 2021). This highlights that domain adaptation is particularly important when low latency at inference time is required, whereas general purpose models can be preferable when quick inference is no hard requirement. Pretrained models as AlignScore (Zhal al. 2023) and MiniCheck (Tang et al. 2024) strike good balance between inference time and performance when generation and fine-tuning is not possible or too costly.
Limitations. In our study we assume that the distribution of evidence samples including the re- trieved documents is readily available. In many real-worke applications, this may not be the case. To address this, techniques like passage clustering and summarization, as explored in [aatti et al. (2024), could be employed on the knowledge base to cover the diversity of evidence passages as surrogate of this distribution. In general, we advise practitioners to obtain the most representative corpus of the target domain for best results. Currently, domain adaptation requires model for each individual domain. Future work is required to further study if it is possible to adapt efficient models for multiple domains without performance degradation. In addition, an assessment including the entire RAG system with a retrieval component using frameworks such as RAGChecker (Ru et al. 2024) may result in insightful in future work.
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A ADDITIONAL RELATED WORK
Automatic Data Selection. A related stream of research is concerned with automatically selecting subsets from large datasets for training. For instance, Autmmngment (Cubuk et al.) 2019) searches for optimal image augmentations through reinforcement learning, but can be computationally intensive. In contrast, Xie et al. (2023) propose efficient importance-weightnnn criterion based on hashed n- n-gram distributions using Kullback Leibler Divergence (KLD). Data selection is linked to works on data valuation, e.g., WWang & Jia (2023), data valuation scores can be used to select training data, often resulting in improved performance.
Classical Unsupervised Domain Adaptation. Beyond synthetic data approaches, classical unsu- pervised domain adaptation (UDA) techniques have also been applied in NLP.[Cher et al.] (2018): [
al. (018); Choudhry et al. (2022) have explored Domain Adversarial Neural Networks (DANN)
(Ganin et al.) 2016), which incorporate domain discriminators during pretraining to learn domain- invariant features. He al. (2020) introduce
(SiFT) which extends the Virtual Adversarial Training (VAT) framework of Miyato et al. (2019) and Jiang et al. (2020) to im-
prove model robustness and generalizability. Techniques like CORAL (Sun Saenko 2016) align feature distributions between source and target domains by matching their second-order statistics.
Finally, domain-adaptive pretraining (DAPT) and task-adaptive pretraining (TAPT) (Gururangan) et al. 2020 Han & Eisenstein 2019) involve pretraining target domain text before fine-tuning on labeled source data, Although these methods have shown success in tasks like sentiment analysis and text classification, they have not been comprehensively studied in NLI.
B THEORETICAL CONSIDERATIONS
B.1 DERIVING OUR OBJECTIVE FROM DISTRIBUTION MATCHING CRITERION
In this section, we present more formal derivation of our objective Lio from an enhanced distribution matihiinnnn-athing objective. We decrease a statistical divergence between a parametric dis- tribution represented by the selected synthetic data samples po (for instance, their Parzen -indoww
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estimator or MLE estimate of a parametric family) and target data distribution providing the gions in feature space that we would like to cover, denoted by Peov. We now consider c to be a vector in continuous vector space. Such mapping can be realized through stochastic encoders / decoders. As we consider each evidence e independently, we write p y) as a shorthand for Po(c, yle). Additionally, we encourage generation of samples where the pretrained model f is not performing well yet by including the cross-entropy loss of the model as a utility term, Ug = CEIf(e, c), where y is the assigned hard label of synthetic sample. In summary, we propose optimizing the distribution parameters Q to minimize the objective Liot.
min D (P2,e(e, y) y)llpoov,eess v)) ((c.)))po. [U,(c,8)) = min Ltot (P Peow,e: S) Q
(4)
where Uy is an additional per-sample utility term that depends on the model f. We omit from the subscript to shorten notation, but still consider fixed evidence e. Since we do not have labels for the target samples, we can't 't estimate the target distribution term Peov (x: y) in the distribution objective Litt- However, we can decompose the divergence using the "chain-rule" of the KLD (Thomas & Joy 2006) into marginal matching term and a label correctness term:
DEL (PollPeor)
DKL (po(c))lpeod (c)) + Ee [DKL (pe(y)))lleon (vlc))
(5)
marginal matching
label correctness
Intuitively, the marginal matching term requires the synthetic samples' features to be close to the range we are interested in covering. We can compute this term by introducing tractable parametric densities. The label correctness term penalizes divergence between the conditional label distribu- tions. Intuitively, it enforces that the samples' labels correspond to their true labels and penalizes uncertainty in the label. We propose to model this label uncertainty using weak entailment certainty estimates r and provide details on how we model both terms in the following paragraphs.
Parametrizing densities. We need insert suitable and tractable parametrizations of Peov and Pa. We start by modeling their marginals. To model Peov we chose an efficiently tractable density Peov (z) can be defined via the nearest target feature vector in claim(D,,.) by
1 Pooy, og (c) = Z exp
|| (- (-
arg min ED, d(c, c)13
(6)
of
where Z is normalization constant. We show that finite Z always exists in the Appendix(B.6
The constant og > 0 will be treated as a hyperparameter in our framework. Let Q C X denote a finite set of selected samples. For po, we chose standard kernel density estimator with kernel width or 2 0:
, (c)
NCc: é, o
Q
GER
Modeling label correctness. We propose to model to label correctness term using the entailment certainty scores, which provides us with an estimate of how well the true and the assigned labels are aligned at a certain point. a positively labeled sample has very high entailment certainty or negatively labeled sample has very low entailment certainty, the assigned labels likely match the ground truth and divergence between true conditional label distribution and assumed distribution is expected to be minimal the sample æ. We derive relation between the label correctness term and our entailment certainty score in form of function Dxz (po (y c) (wlc) = LDiv ÿ). relying on the current entailment certainty (())) and the assigned hard label ý in Appendix B.2 that depicted in Figure 4 The resulting relation fulfills certain natural axioms including that the label correctness term is O, when we have perfect certainty, i.e., LDiv(0, 0, 0) 0, LDiv(1, = 0.
B.2 MODELING AND TRACKING LABEL UNCERTAINTY
In this section we provide strategy to estimate the label correctness term in Equation (5) which is given by DKL (po(y(c) | (yl (see Appendix B. for why we need to model this term). We
we now assume E X' to he in metric space. This can be achieved using encoder mapping the textual input real vectors.
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20
0.5
10
0.0
(a) High Certainty
(b) Medium Certainty
(c) Low Certainty
Figure 5: We model the label uncertainty through a hyper distribution over the parameter yp
need to model both (e(vec and (co)(ylc) to estimate this term. As they are binary, we choose Bernoulli distributions. Our estimated conditional Po(y 1/c) do modeled through Bemnuuli distribution with parameter dg. This conditional distribution is assumed not to change through the augmentation once initialized (because we also do not change the hard labels during augmentations). Reasonable choices for Φu involve setting hard probabilities, i.e., do = ÿ or using the initial label certainty score du = (0) as a softer version.
Unfortunately, we do not directly have access to the true label distribution (eoe(y)c), but we can follow the following intuition: When we arrive at r z 0.5 due to many augmentations, this indicates no knowledge about the ground truth label of c. However, this does not mean that the ground truth distribution Peor (y/ = l/c) = 0.5, for instance the sample can still have certain label that annotators would agree on. Instead there is uncertainty about this distribution' parameter Peo (y 1/c) = 4p- There are different options to model the uncertainty over the true label distribution in this work.
We choose the Beta distribution, which is commonly used as a hyperprior for Benoulli distribu- tions. We chose impose two constraints on the distribution and show that they uniquely define the hyperparameter distribution and have some intuitive properties.
Proposition 1 Let do Beta B) denote Beta distribution. Let do be the parameter of the (certain) initial label distribution (usually corresponding to i)) and let T denote the probability of the mutated sample having label y (entailment certainty).
1. If E (0.5, do), max(@o. 0.5)], there exist unique values for a', such that Elp(y 1|c; vp] = E[Ø] r with mode Qu-
2. For - 0.5. the distribution Betala', 33) with the values from statement I converges unit distribution on [0.1] in distribution.
3. Using Pemly = 1/e) = 4. &o ~ Beta(o', ,8), poly = 1/c) = Po and the expected KLDD over the prior has the closed-form solution
(8)
-H(polylc) Po(y (()())))) -Po 1[e)((a') + =bla' + 3").
(9)
In the last statement, &b denotes the diganma--function and Poly e) Bernoulli(gu) is the initially assumed label distribution for synthetic samples.
See Appendix B.5 for derivation. The convergence behavior of this scheme unit distribution is visualized in Figure [ Using the above update rule and the uncertainty estimation, we can compute the label correctness term for (2 1/c)]
LDiv(r, Φo) H/ (Bernoulli(00)) -(1 (0)v((3(())) (ov((a(rr)) ((a'(r) ( (10)
where a' (r) and (r(r) are the numerical solutions of Proposition 2. To arrive at the formulation in the main paper, we can plug in Φo which is the term visualized in Figure
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B.3 OPTIMIZING THE OBJECTIVE
With models for the terms in the objective at hand, we can select set of most promising samples Q by solving the discrete sample selection problem
Liot Pao, l). @CD 2 10==K
min
(11)
To make the problem computationally tractable, we particularly interested in estimators that de- compose over the individuals samples present in the set Q. With such a decomposition at hand, each sample is assigned an individual contribution and we simply select K samples with lowest individual to contributions to minimize the objective. We derive the following proposition for de- composing our objective using the parametrized distributions with parameters Ør. Ags De, for po and Poov introduced earlier.
Proposition 2 As of - while o² > is constant, the objective converges to
lim Lio (po Pam f) C Σ
[d(è, c)in, + XgLDiv(ra,, 0.) X,U,(e, 9:)]
C.B.EE
(12)
is constant, and )g(og), Xy hyperparameters,
Cuin, e' e'Eelaim(
arg min d(c', Gi),
(13)
and LDiv denotes the expected KLD of the conditional distribution (label correctness term) of the objective that can be modeled as a continuous function from the entailment certainty scores T.
We derive this proposition in Appendix B.4 In summary, we show that for small the contribution of a sample to the objective approaches a sum three parts: The distance to the closest sample from the target claim set for evidence e, claim(D;,)) to the claim é, the label correctness term, and the negative utility. We use the above decomposition in our algorithm, ensuring the objective can be solved highly efficiently in three steps: (1) Computing each samples contribution to Liot. (2) ranking the samples by this contribution, and (3) finally selecting the top-K subset of samples with the lowest contributions.
B.4 DERIVATION OF PROPOSITION 2
To reduce computational complexity, we use an approximation of our objective that does not feature dependencies between the points in the subset. Then the objective is given by a sum of values for the individual points. To derive this objective, we consider the behavior of the objective for a,, - while keeping fixed og > First we note that the normal density PQ.,, (c) with center e and covariance of converges Dirac distribution and for a continuous function f Rd with the filter property:
lim I
(e)d f(c).
(14)
RN
We now consider the individual terms of the objective.
Marginal Matching. Let start with the marginal matching term of the objective.
(x(x) = [
Σ Cic:cr,0,11 = Σ pr(C)
(15)
&EE
EEE
where pi(c) corresponds to the density of the ith mixture component.
DKL. (po(e)]leeoree) Be-pa [log Po(c) - log Powv (c))
(16)
Σ Bong, [log log Poor (c))(c))
E [logpe(e]] - Eoup, [log Peov Peov(c))
O
2,E9
6:E0
(17)
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We need to find good and tractable approximations of both tenms. For small or 0, p: approaches a dirac distribution òø, with all mass at the center 0, This simplifies the objective to
Ee-p, [log [o))] Be-p, [log):(e)] zd log ((2Te) + of) _ log((ee))
(18)
where d is the dimension of c. The second part converges
1
||c:
Cmiu, 122
E Eewp, [log Peov (c)]
(19)
Z
o
where
Cminn
arg min d(c', EclaimD...
(20)
Label Correctness Term. We model the uncertainty propagation as in Equation ar -> O, we have
10
Approaching
Ee-na [DE (po(y) /Pow (y)c)) Eeups [LDiv(r,
(21)
[Div(rs, 9:)
(22)
Utility Term Finally, the same can be done for the utility term:
Ae [U](c,v) + Q
Σ
Nu U,(,000))11
(23)
Assembling all terms. In summary, we arrive at
[ Cmin,
2
Liot
log(2 +
LDii(rr, XoUy((e (24)
o²
Nu
log (2Te)
Q l1: ?
Cmin, 1|
LDiv(r;:0))
(C U:) (25)
2
[
e
C+
Σ
cmin.
AoLDiv(ra, 0.) X,U,((;; 9n) (26)
where in the last step, we multiply all terms be 12] to normalize the first constant to 1. This completes our derivation.
B.5 DERIVATION OF PROPOSITION
Statement 1: We know that the mean of the beta distribution & Beta(o, 3) is given by
El(p]
(27)
and the mode is given by
mode(yp] + 3-2
(28)
for a, 3 > 1 (for a = B = 1, we obtain the uniform distribution and any value is mode).
Constraining the mode to mode (xp) = de yields
g0a 1,3 q(1- oo) 1, [0, 00)
(29)
For the mean, obtain
El
(30)
q + 2
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Setting Blyp] yields
1- 2r (g + 2)r - 9:00 + + g(r - (o) = 2r +
(31)
r 500
The solution of is non-negative if r * Po and if Pu > in case r > 0.5 and if Po < in case r < 0.5. Under these conditions, we obtain the unique parameters
2r (1 Pn) +1
Po + 1, = P0
(32)
40
Statement 2. We prove this statement using the Method of Moments showing that each moment of the distribution converges to the moment the uniform distribution. this is the case, the method of moments asserts that the sequence will converge in distribution. Note that both distributions uniquely determined by their moments because they reside on the interval [0.1]. We see that as 0.5 have that + O. We will show that
Beta(ggPo, q(1 po)) -
Unif(0, 1]
(33)
We therefore compute the nith moments of the Unit distribution for a E N with X Unif(0, 1]
1
(34)
+
For the Beta distribution with with ø ~ Beta(giPo) q(1 (o)) we have
94o + 1+
E
(35)
q g+2+k
k=
Taking the limit results in
T1 n-1
9ia + +
72 k
lim 8
1
lim 7-0
(36)
9 2+2
2 + k
n2 +
Om
Am=
Statement We calculate
Ep DDx (o((lle)lle..
(37)
Pe(y = (lcc 0/c) (log] 1 +))
(38)
+ P = 1 = 1/c) log pl) (lc)(v(3)
(39)
H((e(ylc)) -elll)))))e(eea-- -p(v)) viaa + B)) po ssa 8))
(40)
= ((Po(ylc)) PelV (3)) - polv = 1[c)/((a) [c)) +(a+
(41)
We use the identities:
(ep log vl +(a) v/(a + 3) Eup-Beta(aa.)) [log - (p] = ((3) </ + ())
(42)
(43)
Definition (Probabilistically Correct Data Augmentation, PCDA) A probabilistically correct data augmentation is (potentially randomized) mapping M X x y + X [0, Applying (20++1 ) ) = M (xx. y) generates a modified sample a and additionally returns probability Tmmis [0, of flipping the assigned label during the augmentation step when keeping the mecha-
nism g and the annotator z) fixed, i.e., p(g(#x41, + g(xx, -1) = -ndeer where the randomness is
over the data augmentation output PE+11 Setting the initial agreement Pagice 1.0, we can perform the following update rule
(0)
(k+1) -1
miiss }D Pagiee miso (1 (A)
(k)
(R)
(K)
(44)
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B.6 EXISTENCE OF NORMALIZATION CONSTANT
We consider the density
(-1 - arg min ED. ED., d x:) |
Poos (x) exp
(45)
o
where D; is finite set. We show that the normalization constant exists by proving that the integral of the non-norme nonmalzall density over the feature space X' = IR is bounded. To do so, we perform the following derivation:
[ Rd
exp
arg min ED. d(x,, x)12
dx <
Σ TiED
exp
dx
(46)
0
Rd
o
Drl
(47)
O
The first step uses the insight that the argmin will always any point Dr so if we add up the contributions for all possible points, will arrive upper bound. This completes the proof.
C IMPLEMENTATION DETAILS
C.I IMPLEMENTATION DETAILS FOR AUGMENTATION STRATEGIES
In this section we provide additional details regarding the data augmentation strategies that we de- ploy this work. We fully commit to open-sourcing our code to reproduce experiments upon acceptance.
instruct the LLM (Claude3-Haiku) to create different versions of document where some parts are masked. We decide to mask a random 20% of consecutive words in the document. We let the LLM generate 3 outputs each for 2 different masks, resulting in total of 6 rephrased versions for each claim. Sampling temperature is set to 1.0.
Partial Rephrasing with LLM. We use the prompt given in Appendix H.3
Complete Paraphrasing. We the T5-based model obtained here paraphraser to gener- ate 3 rephrased versions of each claim. To ensure no duplicates are produced, we set parameters and no_reppatt_ngram_sshi
repet iopetitions sepeetitiio__pallyyyll
Drop Sentences. For this augmentation, we sentence tokenize the claim using spacy with en_core wwc.coreeeeeeeeeeeeeeeee sm tokenizer. We postprocess the outputs slightly to better handle statements in quotes. We then randomly drop sentence from the claim.
C.2 DATASETS
We apply the following preprocessing to the datasets: We filter out all samples, that have more than 1022 BART tokens (filling the 1024 context length with an additional SEP and CLS token). The sizes and source links the resulting datasets are provided in Table 4 We note that this reduces the number of usable SummEdits domains from 10 5 (due to some domains only containing overlength evidence documents).
Splits. We either use the available train/test splits (RAGTruth) create splits making sure that summaries / answers derived from the same evidence are either only present in the train or the test split. The validation split derived from the train split.
Processing of QA datasets. The QA datasets require integrating the question and the retrieved documents into single prompt. The RAG-Truth dataset already provides integrated prompts which we use. For the LFQA- Verification questions and documents are provided seperately. We use the integration template "You are given the question: <QUESTION> Here is some information
related to the question: <EVIDENCE DOCUMENTS>.
https::/huugiingfacc. htt.:/aaa...c.//aaaaaaeeee http:a//oohhhoaeeoaaaaaaaaaaaaaa
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Dataset
Train
Val
Test Link
2578
125
636
ragtruth-QA
3661
143
875
summedits
2671
60
733
Ifqa-verification
171
35
65
Lu httpp///ggthaab..o////imommoeee
Vesificaliin/
Table 4: Dataset sizes.
Dataset Parameter RAG-Task
RAGTruth
SummEdits Summary
Summary
QA
QA
Samples per evidence
8
32 3552 (2671)
# Synth. Dataset size (Org. Size)
3544 (2578)
5032 (3662)
336 (171)
# Augmentation Iterations
Table 5: Fixed hyperparameters dependent dataset. Note that set only "Samples per evidence" which determines synthetic dataset size together with the number of evidences.
C.3 AUTO-GDA DETAILS AND HYPERPARAMETERS
We implement the algorithm outlined in Algorithm [ We emphasize that fix the teacher model to assign the initial scores. Here we can compute estimates of the model performance using the validation set of evidence-claim pairs which have access, allowing us chose the best per- forming one as teacher. However, we do not know the performance of the models on claim-clati pairs, so we treat the teacher model used in the augmentation step hyperparameter, that will be optimized.
Fixed Hyperparameters. We additinally keep the following hyperparameters fixed across datasets:
Finetuning: 1 Epoch, learning rate 10-5 5 for DeBERTA, BART, 2 x 10-4 for FLAN-T5, batch size
To compute the distance function we use embeddings from sentence-tS tentteenceet--baseee mode
Number of offspring per sample (1 in pseudocode): 1 = 12, with 6 child samples from LLM Partial rephrasing, 3 each from Drop Sentence and Complete Paraphrasing
We set the number of evidences used per claim which determines size of the synthetic dataset ac- cording the different datasets given in Table Setting chosen values results in the synthetic dataset being between 1.3-times and 2 times as large as the original dataset based on the oberserva- tions in Appendix D.6 suggesting that this is the optimal range.
Optimized Hyperparameters. As outlined in the main text, we apply optuna for 50 configuration trial hyperparameter optimizer to find the remaining hyperparameters. We the ranges X, [0.1, 100], Au E [0.01, 5000] and let the augmentation teacher model be selected from (vectara, alignscore, deberta). We don't allow LLMs teacher models for augmentations because would be too expensive as a lot of augmented samples are created in the course of the algorithm. The final hyperparameters found through optimization are given Table
Base models. As basis for finetuning we use huggingface checkpoints for DeBERTaVe BART- large") and FLAN-TS
Hardware. Our experiments (including runtime) were run on system with 16-core Intel(R) Xeon(R) CPU E5-2686 processors (2.30GHz) and a single Nvidia Tesla V100 GPU with 32GB of RAM.
http:///hggnnoccce//eeeetttttsss httaas//aaggiifaaaccc///ceeetttr long-nli
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Dataset/Task
Initial Teacher Model
Augment. Teacher Model
Ad
Au
Main Results, Table
best NLI model as initial teacher)
RAGTTruth-QA
vectara
debertav2
32.67
20.57
RAGTruut-Summ
vectara
debertav2
198.85
19.51
LFQA-QA
alignscore
vectara
25.27
6.83
Summedits-Summ
alignscore
bart-large
0.02
92.11
GPT-4o teacher results, Table 9
(GPT-4o as initial teacher)
RAGTrruthSSumm
gpt-4o
vectara
0.06
7.58
RAGTruth-QA
gpt-4o
bart-large
47.26
0.15
LFQA-QA
gpt-4o
debertav2
3940.60
7.28
Summedits-Summ
gpt-4o
debertav2
0.01
29.42
Self-super results, Table (DeBERTa as initial, augmentation teacher)
RAGTruth-Summ
debertav2
debertav2
296.13
1.03
RAGTruth-QA
debertav2
debertav2
4591.98
0.24
LFQA-QA
debertav2
debertav2
890.45
17.29
Summedits-Summ
debertav2
debertav2
871.77
19.23
Table Tuned hyperparameters. Bold parameters were fixed for the runs, while the remainder was tuned using the hyperparameter optimizer.
C.4 ALGORITHM
Algorithm Automatic Generative Domain Adaptation (Auto-GDA)
Require: Set of target features Dr, no. best neighbors to select K, no. of augmentations sample 1, Generator G, augmentation modification function M, teacher model T, base model f
1: D (
2: for Each unique evidence e in D, do:
3: Úk +- Bemoulli (0.5), Yk = 1...K
Sample K labels b Sample initial claims using generator g get their label probabilities p()))
4:
é < G(e,, claim (Daim) 0k), Vk- 1...K
(0) T(ck, ūk), Vk 1...
5:
T
(0) { (ér.ir. kk k=1
(0) K
6:
7:
i -0
8:
while Lior (D(
(1)
has not converged do
9:
i i i+1
D(90) - - (i-1)
10:
for (êx, T (( E (W!i-1) do
11:
for times do
13:
è' = ((xk)
> Augment sample through mutation function
(() rk (i-1)(e, àx) T(Ck, 2¹)
+ (1 rk (i-1),
(e, 2x)) T(ēx, ě'))
15:
D. ( u ((e', ((),)))) 9x)}
update and append sample
16:
end for end for
17:
18: D +- arg 20 |0|= Lii
D Select best sample subset
19: end while
20: DUDUD
21: end for
22: f'= fine-tune(f, D)
D Fine-tune model f on synthetic dataset D
23: return f
We provide pseudocode for algorithm in Algorithm
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C.5 BASELINE NLI MODELS
For the complex NLI model baselines, we use Vectara HHEM-2. The model cannot be easily fine-tuned because uses custom code. Additionally we use Alignscore- base with the checkpoint found in this repository [ with the recommended "split" (pre- and postprocessing) nli_st option. We neglect the larger version as its runtime was comparable to I.LMs at a usually lower perfor- mance, making the smaller model better trade-off. Finally we use the best-performing Minicheck flan-t5- -argge model by Tang et al. 2024) from the official huggingface pag
C.6 ROBUST PRE-TRAINING AND FINE-TUNING FOR UNSUPERVISED DOMAIN ADAPTATION
To address domain shift in NLI, we experimented with multiple classical unsupervised domain adap- tation (UDA) techniques, which aim to improve the model's generalization for out-of-domain data by adding robustness during training and by using unlabeled target-domain data. Specifically, we im- plemented Domain-Adaptivy Pretraining (DAPT), Virtual Adversarial Training (VAT), Deep COR- relation ALignment (CORAL), and Domain Adversarial Neural Networks (DANN) combined with conditional entropy minimization.
Notation We refer to source domain data {(z,, K= E (Xs, Ys)", where H; denotes the in- put text and y/ E {0, 1} the corresponding entailment label, and target domain data {zz)) E (Xr))". The labels y E f0, }} correspond to whether a claim is entailed or hallucinated (contradic- tory or neutral). Our goal is to train a feature extractor fe. parameterized by 0, that performs well on the target domain.
Pretraining (DAPT) Gurrrangan et al., (2020), performs Masked Language Modeling (MLM) on (unlabeled) data from the target domain before finetuning on the labeled source-domain data for NLI. This way, it learns the representations of both source and target domain, before finetuning the source domain to relearn classification for the NLI task.
Domain-Adaptinn Pretraining (DAPT) Our first approach, Domain-
Virtual Adversarial Training (VAT) Our second approach is Virtual Adversarial Training MMyytto ct al. 2016), which increases model robustness by introducing adversarial perturbations to the input data during training. Specifically, add adversarial regularization term to the classification objective, which becomes:
min
E( min Ds [e(fo(x). ((
+ EENDs max (x (6),
(48)
WES
where é is the classification loss (e.g., cross-entropy) on the source domain, fKL is the KL divergence between the output distributions, ò small perturbation constrained within ball S, and X the regularization weight. According to (Tiang et al. 2020). VAT induces Lipschitz-continnie means that small changes in the input do not cause disproportionately large changes in the output, improving robustness and generalization for out-of-domain data.
which
Deep CORAL second-order statistics (covariances) of the source and target embedding distributions by minimniz- ing the Frobenius norm between their covariance matrices. Specifically, denoting Cs and Cr the covariance matrices of the embeddings of the source and target samples extracted from the last encoding layer, respectively, and as the dimension of the features, the regularization loss is:
The objective of CORrelation ALignment (CORAL) (Sun et al. 2016) is to align the
1 Arl
LCORAL (0)
Cs Cril.
(49)
Domain Discriminator and Conditional Entropy Minimization Finally, we also implement domain discriminator inspired by domain adversarial training (Ganin al., (016)) and other related works in NLP. The discriminator trained to classify source and target domain features correctly,
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whereas the feature extractor (classifier) fo is trying minimize the discriminator's accuracy, which should amplify the learning of domain-invariant features from the classifier. The domain adversarial loss is:
La(0) = EE D(f(x()) Ez [In(1 -(fo(=)))
(50)
We also experiment with adding a conditional entropy loss to ensure the model makes confident predictions on the target domain and improve the placement of the initial boundaries, as outlined in Shu et al. (2018) and Reed et al. (2014):
Lc(0) = Er [fo(z) T In fe(x))
(51)
Implementation Details For our robust optimization experiments we used the DeBERTaV2 based NLI model, and limited maximum tokenization length at 1,024 tokens across all benchmarks. For DAPT we extracted the DeBERTaV2 backbone and trained on the target domain for full epoch, using 10% masking probability. the tine-tuning stage of both methods we ran 1 full epoch on the MNLI dataset. At the masked pre-training and fine-tuning stages of the experiments, we used the AdamW optimizer with learning rate 10-5 and weight decay 10-3, enabling 100 warm-up steps over the supervised fine-tuning. For SiFT and CORAL set the coefficient of the respective regularization terms to 0.5, after running hyperparameter optimization with a coarse grid search. Batch size used for covariance estimation in CORAL was set at 64. Each experiment was repeated over 3 random trials.
D ADDITIONAL RESULTS
D.1 ADDITIONAL METRICS
We chose the Area under Curve for Receiver-Operaton Characteristic (AUC-ROC) as main met- ric, as it is less dependent on threshold calibration and also works for imbalanced datasets. We report results in other metrics such as balanced accuracy without threshold calibration (using 0.5. as a threshold as suggested in Tang et al.] (2024) in Table 7 and F1-Scores in Table [8] The results highlight not only that main results valid across different metrics in uncalibrated balanced accuracy, our models trained with Auto-GDA data even outperform LLMs by an average 3.4 accuracy percent points.
D.2 ADDITIONAL QUALITATIVE RESULTS
Estimating the mislabeling probability. An integral part of our algorithm the estimation of the agreement probability in Equation (44). To investigate the effeet of implementing this choice, run an ablation study to better understand how the quality of the agreement probabilities affects the score. We provide results average over 3 runs in Figure 7a The results indicate that the choice of the model used to estimate r the label certainty score has substantial effect on the quality of the results. While the utility (in terms of ROC scores) drops when noise is added, it increases again when high levels of noise are applied. We attribute this behavior to the algorithm neglecting the mutated samples almost entirely when the noise level too high and mainly selecting the few shot generate samples. As shown in Table 2 these have fair utility already.
Using one vs. several augmentation routines. A key design goal of our algorithm was the ability to automatically select the most promising augmented samples. We investigate the effect of using only single or several augmentations in Figure 6al The results highlight that the Partial Rephrasing augmentation with LLMs as well as the sentence deletion augmentation seems to be most successful. The Complete Paraphrasing augmentation leads to substantially lower data quality on own. How- ever, the best utility is achieved when all three augmentations are combined. We study the origin of the samples eventually selected by our algorithm and find that the usefulness of the augmentations on their own is reflected by the share samples selected from each of the augmentations depicted in Figure (b)) Together, this highlights that Auto-GDA succeeds in selecting the most promising samples generated from the augmentations automatically.
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Dataset
RAGTruth
LFQA- Verif.
SummEdits Summary
Avg.
RAG-Task
Summary
QA
QA
complex roluennss hate modele
FLAN-TS
0.666
0.636
0.618
0.646
0.641
DeVERTaV2
0.727
0.505
0.588
0.810
0.658
BART-large
0.604
0.633
0.782
0.625
0.661
DAPTDBBERTAY
0.677 + 0.04
0.654
0.033
0.748
+ 0.066
0.792
+ 0.0s
0.718 0,02
DiFERaBER
0.716
+ 0.09
0.562
0.006
0.810
+ 0.005
0.806
+ 0.01s
0.724 0.016
CORALDBBRRW
0.657
+ 0001
0.637
0.02
0.815
0:011
0.792
0001
0.725 0.01
MiniCheck-TS
0.675
0.600
0.564
0.679
0.630
AlignScore
0,572
0.650
0,594
0.770
0,646
Vectara-2.1
0.662
0.744
0.618
0.581
0.651
Anto GDN
Flan-T5 (Auto-GDA)
0.650
+: 0.703
0.019
0.669
i: 0.066
0.761
+ 0.000
0.696
BART (Auto-GDA)
0.710
+ 0,088
0.794
2011
0.772
+ 0.033
0.798
+ 0014
0.769
DeBERTaV2 (Auto-GDA)
0.737
+ 0.08
0.784
+ 1011
0.776
+ 0.022
0.817
+ 0.099
0.778
GPT-4o
0.691
0.764
0.688
0.835
0.744
LLMs
GPT-4o-mini
0.666
0.684
0.625
0.832
0.702
GPT-3.5
0.593
0.586
0.611
0.723
0.629
Table 7: Performance comparison to baselines (uncalibrated balanced accuracy). In this metrics, models even outperform LLM baselines.
Dataset
RAGTruth
LFQA- Verif. SummEdits
Avg.
RAG-Tash
Summary
QA
QA
Summary
base modeel
FLAN-TS
0.890
0.900
0.705
0.550
0.761
DeVERTaV2
0,897
0.899
0.705
0.748
0.812
BART-large
0.893
0.900
0.846
0.641
0.820
rohustieess
DAP'TDBBRTMN
0.655 + 0.00
0.489
+ 0.16
0.748
+ 0.076
0.762
+ 0.04
0.664
+: 0.07
SiFT DiBRT
0.704 0.19
0.362
0055
0.810
0.762
0,660
0.099
CORAL
0,556 + 0.01
0.487
1: 0.02
0.815
+: 0.001
0.752
+: 0:01
0.653 1: 0.06
MiniCheck-TS
0.897
0.901
0.743
0.682
0.806
complex
AlignScore
0.888
0.903
0.847
0.744
0.846
Vectara-2.1
0.910
0.921
0.702
0,498
0.758
Auto GDA
Flan-T5 (Auto-GDA)
0.901 0:0
0.905
0.00
0.699
0.693
no15
0.800
BART (Auto-GDA)
0.910 + oom
0.923
+: 0.00
0.857
+ 0.177
0.725
- 0.10
0.854
DeBERTav2 (Auto-GDA)
0.912
0.930
0.854
0.750
0.861
GPT-4o
0.929
0.914
0.848
0.782
0.869
LLMs
GPT--4omini
0.918
0.909
0.767
0.764
0.840
GPT-3.5
0.887
0.899
0.746
0.687
0.805
Table 8: Performance comparison to baselines (Binary F1-Scores).
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Dataset
RAGTruth-QA DeBERTa BART-large Flan-T5
Model
mean
No Augmentation
0.836
0.845
0.772
0.818
06-
ILMPaatialRephrase
0.869
0.890
0.767
0.842
Complete Paraphrasing
0.845
0.863
0.711
0.806
DrupSentence
0.868
0.872
0.758
0.833
(b) Composition of the final dataset by ori- gin of the selected samples (augmentation routines few-shot prompting without aug- mentation). The selection corresponds well to the usefulness of the augmentations on their own.
All
0.872
0.886
0.806
0.855
(a) Testing effect of using one vs. several augmentations. On average the best results are obtained when combining sev- eral augmentations.
Figure 6: Qualitative Results on sample selection: Our framework succeeds to automatically select the best samples from different augmentation strategies outperforming single augmentation strate- gies.
Dataset RAG-Task
RAGTruth
LFQA- Verif.
SummEdits Average Summary
Summary
QA
QA
Teacher Model Used
Vectara-2.1
Vectara-2.1
AlignScore
AlignScore
Teacher Performance
0.805
0.854
0.904
0.894
0.864
DeBERTaV2 ADAA
0.837
0007
0.867
0.007
0.925
0.009
0.883 + 0.05
0.878
Teacher Model Used
GPT-4o
GPT-4o
GPT-4o
GPT-4o 0.880
GPT-4o Performance
0.892
0.865
0.896
0.883
DeBERTaV2 (Auto-GDA)
0.808 2017
0.855
0.003
0.910 0019
0.887 0004
0.865
Techer Model Used
DeBERTaV2
DeBERTaV2 DeBERTaV2
DeBERTaV2
DeBERTaV2 Performance 0.782
0.530
0.645
0.876
0.708
DeBERTaV2 (Auto-GDA) 0.830 0.009
0.807
0.010
0.923
0.012
0.890 4: 0.07
0.863
Table 9: Using different teacher models in Auto-GDA to fine-tune DeBERTaV2. In the upper part we add best results from Table I for comparison. In the center part, we highlight that using GPT-4o as teacher model to assign intial probabilities does not yield substantial improvement. However the lower part shows that it is possible to do self-improvement using only DeBERTa as teacher model for both initial scores and augmentation scoring.
D.3 DIFFERENT TEACHER MODEL.
We investigate the application of different teacher models in Table Our results indicate the learn- ing from GPT models works in general, but does not results in better performance that using the best non-LLM teacher. We additionall study self-improvement, using DeBERTa as both a teacher model for initial scoring and augmentation scoring. This shows that improvements thought self-supervision are possible.
D.4 ROBUSTNESS TO INITIAL DATA QUALITY
The initial data plays an essential role in Auto-GDA, however we designed our algorithm to be as fault-tolerant as possible and to be able to cope with some low-quality samples in the initial population. For instance, we do not solely rely on generative models, but use discriminative teacher models in the generation loop well. The selection objective is specifically designed to filter out low-quality samples. Mislabeled samples in the fine-tuning dataset can severely affect performance of fine-tuned model (Wang et al.) 2023b). To test the robustness of Auto-GDA, 50 % of the labels in the initial data to study its effect and trace these samples through the generation process. We use the FrO--eeriiiaationn dataset and perform 5 runs with different initial generation
manually flip
26

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0.840
ragtalllSummury. obj.
regralliQA, cbj.
0.835
ragrath Summary, ROC
ragtruth QA, ROC
2300
0.830
0.86
2250
0.825
0.84
DeBERTaV2
Vectura
AlignScore
2190
0.815
0.0
0.1
0.2
0.3
0.4
0.5
Noise injection
Tennioon
(a) Effect of computing using different models noise levels inserted in the scores. While noisy, the results point to different trends for the teacher models: For the best teacher model, noise hurts performance, whereas for the other ones, does not hurt or might even boost per- formance as more few-shot samples are se- lected.
(b) Evolution ROC scores our objective over three itcrations our algorithm. see that ROC increases as our objective decreases. We stop our algorithm after two iterations, when the objective does not improve anymore.
Figure 7: a) Reliable estimation label certainty is essential for selection of high quality data. b) The resulting synthetic data often contains original few-shot generated samples as well as fair mix of mutated samples generated from them. They are automatically selected by our algorithm.
Dataset
RAGTruth
LFQA-Verification
SummEdits Summary
LAG-Task
Summary
QA
QA
AlignScore 0.737
0.836
0.870
0.874
Vectara-2.1
0.814
0.879
0.879
0.805
GPT-4o
0.828
0.866
0.876
0.878
Our Data 0.837 0.007 0.867 + 0.007
0.925 + 0.009
0.883 + 0.005
Table 10: Comparing our approach to the naive baseline of pseudo-labeling the training data and fine-tuning the DeBERTa V2 model on the pseudo-labeled data.
seeds (using the hyperparameters in Table otherwise). First, while 50% of the data have flipped labels initially, in the data selected after one iteration of Auto-GDA, only 10.0% +- 1.1 % the initial data or augmentations of the data with the flipped labels. After fine-tuning the generated data, the ROC-AUC score drops slightly by 1.1 points as shown in table below.
Original Performance with 50% flipped initial labels
ROC-AUC of fine-tuned DeBERTaV2
0.925 0.009
0.914 = 0.007
Despite the substantial amount of mislabeled data (50 %) the drop in performance remains small, highlighting that Auto-GDA is quite robust to data quality issues due to its fault-tolerant design.
D.5 SIMPLE BASELINES
We compare our results model trained on pseudo-labels in for the original datasets in Table I0 The results inicate that this is surprisingly strong baseline, which is however surpassed by Auto- GDA in 3 out of 4 cases.
Results when the models are fine-tuned on the validations set directly are shown in Table 12
D.6 EFFECT OF DATASET SIZE
When choosing the dataset size we used the data size slightly larger that that of the original dataset orientation. We experiment with different dataset sizes and learning rates shown in Figure 8
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Dataset RAG-Task
RAGTruth
LFQA- Verif. SummEdits Average
Summary
QA
QA
Summary
FLAN-T5
0.734
0.708
0.655
0.700
0.699
Flan-T5 (Auto-GDA)
0.756
0.01 0.783
1 0013 0.687
0002
0.824
0010 0.762 (+0.063)
BART-large
0.696
0.670
0,821
0.769
0.739
BART (Auto-GDA)
0.813
i 0.099
0.867
+ 0011 0.867
i 0.26
0.860
i 0010
0.852 (+0.113)
DeBERTaV2
0.782
0,530
0.645
0.876
0.708
DeBERTaV2 (Auto-GDA)
0.837
0.007 0.867
+ 0.007 0.925
0.009
0.883
0.00 0.878 (+0.170)
Table 11: Direct comparision of improvements
Dataset RAG-Task
RAGTruth
LFQA-
SummEdits Mean Summary
Summary
QA
QA
Non-Fintuned
0.782
0.530
0.645
0.876
0.708
Few-Shot Data
0.799
0.826
0.934
0.872
0.858
FT on validation
0.784
0.750
0.899
0.890
0.833
DeBERTaV2 (Auto-GDA, best teacher)
0.837
0.867
0.925
0.890
0.878
Table 12:
Fine-tuning validation set does increase performance but does not reach Auto-GDAs performance on average.
When keeping training fixed to one epoch, we find that with higher learning rates, smaller dataset sizes lead to higher performance, and with lower learning rate, more data is required which seems natural. Globally, we observe that a learning rate of 10-5 is near optimal, but the performance is rather insensitive This based on a prior observation that significant oversampling of the dataset size had seemingly little effect.
We also study the effect of generating more LLM samples on the performance of the baseline of solely fine-tuning on L..M-generaed data. We generate more LLM data using our few-shot prompt- ing strategy and fine-tune the DeBERTa model on LLM-generated datasets in the range of 0.5x 8x the size of the datasets used in the main paper. We obtain the results shown in Table 13 (ROC-Scores of the DeBERTaV2 V2 model fine-tuned on the data). Perhaps surprisingly. increasing the number of generated samples does not increase the performance of fine-tuned models. We attribute this to the observation that LLMs (even when setting a higher temperature) will generate highly similar sam- ples after certain dataset size is reached, not further improving performance of fine-tuned model. Performance even decreases again, potentially due to overfitting on the synthetic data. We therefore conclude that even with more LLM data, the performance does not reach the level of Auto-GDA.
D.7 ABLATING TERMS IN THE SELECTION CRITERION
To better understand the effect of the different terms in our objective, we perform additional ablation study only using a selection objective with one term a time. We provide the results
dataset size
0.5x
1x
2x
4x
8x
LFQA
0,916 + 0.007
0.924 + 0.006
0.903 = 0.013
0.896 + 0.007
0.868 + 0.003
SummEEdits
0,880 + 0.003
0.876 + 0.002
0.876 = 0.005
0.868 + 0.002
0.855 4 0.005
RAGTruth-Summnn Summary
0.821 1 0.003
0.808
1 0.005
0.791 = 0.007
0.797 1 0.001
0.783 1 0.004
RAGTruth-O.
0.808 1 0.007
0.828 1 0.006
0.830 = 0.006
0.834 + 0.004
0.821 1 0.005
average
0.860
0.856
0.849
0.844
0.841
Table 13: Results when fine-tuning on more LLM generated data (ROC-AUC scores for DeBER- Ta V2) show that performance of this baseline does not improve with more data.
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0.85
0.80
0.70
0.65
I--00"(455) strat
Ir-10"(5.01
I==10"(5.3) stret
0.55
strat
Ir-10" strat
0.50
original size
10-1
examples per evidence
Figure 8: Testing different synthetic dataset sizes and learning rates for fine-tuning. While the original dataset size is at about 3 claim examples per evidence (dashed line) we test a wide range of dataset sizes ranging from 1/50 examples per evidence (100x smaller than original) to larger datasets with 30 claims per evidence, (10x larger than original). We observe relatively stable optimum from about a 1/3 of original size to twice the original size using learning rates around 10-5
dataset
all terms
only distance
only label-cert.
only utility
LFQA
0.925 + 0.004
0.916 + 0.004
0.919 0.003
0.917 + 0.005
Summedits
0.883 + 0.002
0.877 + 0.014
0.866 0.002
0.881 + 0.002
AAGTruth-Summan run)
0.861
0.805
0.860
0.587
RAGTruth-QA run)
0.827
0.756
0.814
0.823
Table 14: Ablation on terms the selection criterion (ROC-AUC scores for DeBERTaV2). Using all three terms results in better performance than only using one of the terms each.
in Table 14 Despite some difference being small, we see that all terms contribute to the final performance.
D.8 EFFECT OF SOURCE DATA ON UDA EFFECTIVENSS
Table 15 compares the performance (ROC-AUC scores) various Unsupervised Domain Adapta- tion (ODA) methods and synthetic data approaches across different source datasets. All results evaluated on the RAGTRUTH target dataset, using DeBERTaV3 model trained on the PAWS, Vita- minC, and Fever data. The table illustrates the effectiveness of different UDA methods and synthetic data approaches when applied to various source datasets (MNLI, Summedits, and More specifically, we that except for the synthetically generated version of RAGTruth, the choice of the source domain data does not seem to alter results significantly. We also see that vanilla fine- tuning on the synthetic RAGTruth data outperforms all other variations, indicating that synthetic data is more appropraite for NLI than traditional UDA methods. This is perhaps due to the fact that very small changes in the generated claim can flip the label from entailed non-entailed and vere-versa.
Method
PAWS++ VitaminC
Summedits
Synthetic RAGTRUTH
No fine-tuning
0.735
0.735
0.735
Vanilla fine-tuning
0.735
0.737
0.844
CORAL
0.682
0.683
0.728
SMART
0.743
0.721
0.833
MLM
0.680
0.577
0.731
Domain Discriminator
0.603
0.712
0.746
Table 15: ROC-AUC scores for different UDA methods and synthetic data approaches.
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D.9 EFFECT OF REGULARIZATION CONSTANTS ON UDA METHODS AND ON FINE-TUNING PERFORMANCE
This appendix presents a comparative analysis of THE UDA methods and their impact on the fine- tuning performance of our model. Figure [ shows the ROC-AUC scores for different UDA meth- ods as we increase the percentage of target domain data used for fine-tuning. We see two things: i) No configuration of the UDA methods improves the model's performance significantly, and ii) the robustly trained models also do not benefit from faster finetuning with fewer samples, as their performance when further finetuned with target-data samples is similar the original model after finetuned on the same splits. In short, we believe that traditional UDA methods do not show promise for the NLI task.
RDCAUC o to Target Outa lond for hrourey
TOCAUC to Percad, of Tared Dutu uvol Predaring
K7-
54-
M4N Lani
CORVA (awt) Biigeal
Sapl Saps Beanee Retarraai ine
taxe Bare togel tate welle Neduc US3
(a) Masked Language Modeling (MLM)
(b) CORAL
Web Wresuning en Source Data with U.A.
(Mier redurinn Seurce Cola with Upes
Hed CaR (4-2
3.5
Usd (o-1) CoN5
02
Sampa No lage Las sed theaa
Samole rai lore own und www.ningggggggg UOA
(c) Discriminator and Conditional Entropy
(d) SMART
Figure 9: Performance comparison of different UDA methods across fine-tuning percentages
D.10 GENERATION TIME FOR AUTO-GDA
To provide a complete picture of our algorithm, we include runtimes that we obtained for each of the steps of ADto-GDA. We run Auto-GDA on the smallest and largest dataset, LFQA and RAGtruth- QA respectively, on our hardware (single Nvidia Tesla V100 GPU, 64GB RAM, 8 Intel Xeon CPUs) and provide individual runtimes in Table 16
E PROMPTS
E. INITIAL GENERATION PROMPTS
We use two prompts to generate intitial samples that differ according to the respective target labels. In practice we use a maximum of few shot samples, or the number of samples available in the train dataset for given evidence e.
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Dataset
Real / Generated Size Initial Generation Mutation Selection Fine-tuning Total (1 iteration)
LFQA 171 336
90.12
188.12
90.97
34.75
403,96
RAGTruthh
3662 5032
650.38
1562.78
1161.11
432.84
3807.1
Table 16: Generation times (sec.) obtained when running Auto-GDA on the largest and smallest dataset on our hardware. The total time for one iteration of Auto-GDA is roughly 4x-6x more than generating few-shot data only. We would like to highlight that runtimes depend rather strongly on the latency of the API calls (and how much they are parallelized).
Positive (entailed) prompt:
Human: You are given the following document wrapped in <document> </document> tags: www.reetttDDOCCECT-CtCCtee
Your task is generate summaries from document. Here are some examples of how the summaries could look like:
Note however that some of the samples contain incorrect information that is not part of the document! Here are the examples:
<example 0>EXAMPPI EEEAMML.EO<//xampl O>
<example 1> </xxM 1>
Now your task is to generate N summaries from the document. However, unlike some of the examples given above, the summaries must be entirely supported by the document. Only include information that is directly inferrable from the document. It is also important that the summaries reflect the style, length and wording of examples. If there are common patterns or sentence structures in the examples summaries, the created summaries should reflect those. Each summary is identified with integer from 0 to N-1. The summaries must be wrapped in <summary #></summary #> tags, where # is replaced with the summary id. Assistant:
To generate non-entailed samples, the following modified prompt is used:
Human: You are given the following document wrapped in <document> </document> tags: <document> Eeireett-EVIDDE EVIDENCE DOCUMENT</doummet> </document> Your task is to generate summaries from a docu- ment. Here are some examples of how the summaries could look like:
Note however that some of the samples contain incorrect information that not part of the document! Here are the examples: <example O>CLAIM EXAMPLE0</xxapple O> <example 1>CLAIM EXAMPLE1<//xampl 1> Your task to generate summaries from the document. However, now all of the summaries must contain at least one piece of non-factual information. This can be some information that is not present in the docu- ment or some information that is contradictory to the information the document, but intuitively appears to make sense. Otherwise they reflect the style, length and wording of examples. If there are common patterns or sentence structures the examples summaries, the created summaries should reflect those. Modify dif- ferent pieces of information at different places in the document. Each summary is identified with integer from to N-1. The summaries must be wrapped in <summary #></summary #> tags, where is replaced with the summary id. Assistant:
E.2 ENTAILMENT PREDICTION PROMPT
We use the following prompt to compute entailments with the LLMs. It stems from Tang et al. (202)) however instead of answering Yes/No the LLM is prompted to anser with "0"7""" which has the advantage that the token probabilities can be used to compute an uncertainty score.
Determine whether the provided claim is consistent with the corresponding document. Consistency in this context implies that all information presented in the claim is substantiated by the document. not, should be considered inconsistent.
Document: EVIDENCE DOCUMENT Claim: CLAIM
Please assess the claim's 's consistency with the document by responding with either "1" (consistent) or "0" (inconsistent). Do not output anything else. Answer:
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E.3 LLM PARTIAL REPHRASING PROMPT
We use the following prompt to instruct the LLM to only rephrase specific parts of a sentence that masked out with
Your task is to fill in the gaps in document indicated with with additional details. If there is gaps, please output the input text. The number of x_ indicates the approximate number of words that should be filled into each gap. While slight deviations (e.g., one word more or less) are permissible, the filled in should respect the length indicated through the number of **Do not change the text outside the gaps and do not include gaps in the final output. ** You will generate N different completions of the document. Each completed document is identified with an integer from 0 to N-1. The document with the blanks filled must be wrapped <answer #></ansser #> tags, where replaced with the id of the filled-in document. You will now see the original document, but will have to generate different versions that preserve the meaning by filling the gaps.
Here is the original: EVVcUMenDEVIDENCCE DOcuMENT-document The document including the gaps is: <documens-OOCUMENT WITH WORDS MASKED</docmenn>>>> Assistant:
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Reviews:
[{"review_id": "a1wEgLbpUP", "reviewer": "Reviewer_Wxqg", "summary": "This paper introduces a novel framework to enhance the performance of NLI models in verifying retrieved evidence within retrieval-augmented generation settings. The paper addresses the issue of performance drop in out-of-domain inputs. To alleviate this problem, the authors propose an automatic generative domain adaptation method to fine-tune NLI models in an unsupervised manner. In the proposed method, this framework considers both diversity and quality by using a sequential augmentation technique and optimizing a distribution-matching objective in the data generation process. Experimental results demonstrate that the NLI model fine-tuned with the proposed method achieves performance closer to that of LLM models without sacrificing efficiency.", "questions": "See the weaknesses", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed method is novel and practical in RAG scenarios.\n- This manuscript is clearly written and easy to follow.\n- The experimental results are well conducted, showing advantages in terms of accuracy and efficiency.", "weaknesses": "- The paper would benefit from a more detailed analysis to clearly demonstrate the robustness of the proposed method across various domains. In real-world scenarios, domain boundaries are often ambiguous, and it’s common to encounter mixed or overlapping domain data. By evaluating the model in a multi-domain or domain-mixing setting, the authors could provide stronger evidence of its robustness and practical applicability in complex, realistic cases.\n\n- It would be valuable to include an analysis of how the model performance depends on the quality of the initial synthetic data generation. If initial data is inaccurately generated, it might negatively influence the model’s performance. An examination of whether the model can correct or adapt to potential errors in this initial phase would clarify the method’s resilience to suboptimal synthetic data.\n\n- The paper’s selective objective function appears complex, suggesting that the model could be highly sensitive to hyperparameter choices. Observing large variations in hyperparameter values for each dataset implies that tuning these parameters may be challenging. Providing further insights into the model’s hyperparameter sensitivity and offering guidelines for tuning could improve the approach's usability and reliability in diverse settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "YNMIqYP6Y2", "reviewer": "Reviewer_ykaR", "summary": "This paper tackles the issue of hallucinations in Large Language Models (LLMs) used in retrieval augmented generation (RAG) applications, where verification of generated outputs through natural language inference (NLI) models is essential. The authors propose Automatic Generative Domain Adaptation (Auto-GDA), an unsupervised framework that generates high-quality synthetic data to fine-tune lightweight NLI models for specific RAG domains. Key contributions include formalizing the unsupervised domain adaptation problem, developing the Auto-GDA framework for efficient sample selection, and demonstrating that models fine-tuned with Auto-GDA outperform weak teacher models and approach the performance of human-labeled references, all while achieving significantly lower latency than LLMs. This work presents a promising solution to enhance NLI model performance in real-time RAG applications.", "questions": "Refer to weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed Automatic Generative Domain Adaptation (Auto-GDA) offers a novel approach to unsupervised domain adaptation, effectively generating high-quality synthetic data to fine-tune NLI models tailored for specific RAG contexts.\n- Empirical results demonstrate that models fine-tuned with Auto-GDA significantly outperform weak teacher models and achieve performance levels comparable to those using human-labeled data, indicating its effectiveness in improving NLI model accuracy.\n- The paper is well written and easy to understand.", "weaknesses": "- The effectiveness of the Auto-GDA framework relies heavily on the quality of the synthetic data generated. Poorly generated data could lead to suboptimal fine-tuning and negatively impact NLI model performance.\n- Although the framework aims to address domain mismatches, (in my own opinion) there may still be challenges in generalizing to highly diverse or previously unseen domains, potentially reducing the model's effectiveness in broader applications. I'm curious how well the proposed method performs under such extreme conditions?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "tAxWGGo1i9", "reviewer": "Reviewer_4u6k", "summary": "The paper considers the problem of domain adaptation of the natural language inference models (NLI) which are often used in retrieval-augmented generation (RAG) to judge the entailment of the generated response from the retrieved context. The paper proposes Auto-GDA, a method for generating synthetic data for a given domain, that can be used for finetuning an NLI model to improve its performance in this domain. The proposed method is iterative and consists of three steps: (1) seed data generation using an LLM; (2) data augmentation e.g. using paraphrasing or sentence deletion; and (3) data filtering, to minimize their proposed enhanced distribution matching objective; steps 2 and 3 can be repeated iteratively. The method is tested on several datasets that provide human labels for NLI, and compared versus existing off-the-shelf systems and several ablations.", "questions": "1. What is the time of running each step in Auto-GDA (steps 1-2-3 and NLI model training), for different datasets (with their sizes)?\n2. One of the arguments for the necessity of domain adaptation in the introduction is that “inputs may follow a specific format due to the RAG prompt template” (line 79). Why not pass evidence and claims to NLI models without this template, if it reduces domain shift and hence improves performance?\n3. What criteria is used to select optimal hyperparameters using optuna? Is it performance on some dataset (which one)?\n4. How do you tune hyperparameters of unsupervised domain adaptation baselines?\n5. Are there particular reasons why Vectara-2.1 outperforms AlignScore on RAGTruth and vice versa on other datasets? E.g. due to some specific training data or algorithm specifics.\n6. Do you plan to release open-source code for the proposed approach?\n[Update during discussion: all questions answered]\n\n\nComments\n* Due to the high amount of notations, it may be hard to follow the method sometimes, e.g. trying to remember what a particular notation means. \n* Line 43: “even when the most capable LLMs are used with RAG, hallucination rates of 20 – 30% persist (Santhanam et al., 2021).”  too old reference\n* Figure 1: “RAG task performance (ROC-AUC).” Unclear x label: it seems that it is RAG performance (unclear how measured with ROC-AUC), but it is NLI performance for RAG as far as I understand\n* Line 233: “we also generate realistic initial samples using LLMs”. Use a more specific term than “samples”\n* Line 384: “Optimizing the objective for a subset Q”. What does Q refer to here?\n* Line 429: better define “complex” category", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* A relevant research direction of adapting RAG components to user domains\n* Comprehensive related work section\n* Detailed description of the proposed approach, used datasets, baselines, and experimental details\n* The proposed method is compared to a series of existing NLI solutions on several datasets, and inference time is also compared for various methods", "weaknesses": "1. The presented Auto-GDA method is rather complex (involving multiple steps and components, including heuristic augmentation techniques) and has several important hyperparameters, such as $\\lambda_d$ and $\\lambda_u$ in eq. (2), population sizes M and K, or the number of iterations. Hyperparameter tuning requires running the proposed approach 50 times (line 446), including training of the NLI model on the generated data (from my understanding). At the same time, improvements over simply using LLM-generated data are quite modest (row 4 vs 2 in Table 2). \n    - Furthermore, the high cost of running hyperparameter optimization, augmentation, and data selection in the proposed approach, motivates the substantial increase of data points in the simple baseline of using LLM-generated data, to make their computational costs similar. This would make the baseline stronger and reduce its difference versus the proposed approach even further. [Update during discussion: addressed in the authors' response]\n    - Performance gains versus out-of-the-box NLI models are also rather small, e.g. comparing the best performing (domain-adapted) Auto-GDA versus the best performing “complex” method out-of-the-box (83.7 vs 80.5, 86.7 vs 85.4, 92.5 vs 90.4, 88.3 < 89.4), or Auto-GDA (Flan-T5) vs MiniCheck-T5 (75.6 \\approx 75.4, 68.7 < 74.1, 82.4 vs 79.1; only one high improvement 78.3 vs 64.0). \n2. I would expect more ablations of the proposed approach, e.g. testing the removal of each of the terms in eq. (2), or on the contrary using only one of these terms for filtering. [Update during discussion: the ablation was provided, showing that the proposed enhanced distribution matching objective seems to be mostly reducible to a single term]\n3. The motivation for the proposed approach is to improve RAG pipelines in domain-specific scenarios, however no experiments with domain-specific RAG are presented to demonstrate these improvements. For example, domain-specific RAG scenarios considered in [1] could act as potential testbeds, e.g. RobustQA [2]. [Update during discussion: addressed in the authors' response]\n\n[1] Dongyu Ru et al. RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation. NeurIPS 2024\n\n[2] Rujun Han et al.  RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering. Findings of ACL 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)