An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation

Ali Rostami

REVIEW 3 major objections 5 minor 244 references

An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A food-specialized LLM recommender, F-RLP, connects food data, engineers counterfactual training samples, and guarantees valid picks by choosing from real options, outperforming generic LLM recommenders.

desk verdict The F-RLP synthesis is a plausible systems contribution, but the load-bearing US4B taste mapping is unvalidated and the feasibility results are not in the review copy, so the 'truly personalized' claim is currently supported by architecture, not evidence. read the letter →

arxiv 2504.20092 v1 pith:UFZSLCQZ submitted 2025-04-25 cs.IR cs.AI

Ali Rostami This is my paper · ORCID

classification cs.IRcs.AI

keywords LLM-basedfoodrecommendationpersonalizedaslanguageprocessingcounterfactualdataengineeringcontext-awarepreferencemodelingUS4BtastespaceWorldAtlasmultimedialogging

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The thesis sets out to build a food recommendation system that works in daily life, which it argues generic LLM recommenders cannot do because they ignore food-specific structure. It claims that a food-specialized framework, F-RLP, fixes this by connecting food data sources, re-engineering training samples with counterfactual 'what if' examples, and forcing the LLM to answer from a list of real, available dishes. Supporting innovations include a multimedia food logger, a geolocation-based World Food Atlas, and a personal food model that splits biological needs from taste preferences. If the framework is right, food recommendations can be simultaneously healthy, personalized, context-aware, and grounded in what is actually obtainable.

What carries the argument

The load-bearing mechanism is F-RLP's three-stage pipeline anchored by its option-list constraint: the LLM never generates a free-form dish name; it receives a bounded list of real options and chooses among them, which prevents hallucinated recommendations. Around that constraint, the counterfactual generation stage re-sorts candidate dishes by priority metrics such as healthiness before preference and uses the top option to build 'what if' training samples, while the context-generation stage feeds the model a personal vector combining a short-term biological component with a long-term preference component expressed in the six-dimensional US4B taste space.

What would settle it

Give a panel of human raters a set of dishes, compute each dish's US4B vector by the molecule-counting method, and see whether the vectors predict the raters' sweetness, bitterness, and umami scores better than chance; disagreement between predicted taste orderings and the panel would collapse the preference foundation, while agreement would give the synthetic-data results real-world backing.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that LLMs can be made to recommend food reliably if the task is treated as a food-specific language-processing problem rather than generic text generation. The F-RLP paradigm has three stages: a context-generation stage that assembles the user's biological and preferential vectors along with the current situation; a counterfactual generation retraining stage that creates improved training samples by sorting candidate dishes against prioritized nutritional and preference metrics; and a query stage in which the LLM is given a list of real options and must pick from it, structurally guaranteeing that the output is an actual available dish. The thesis further claims that this integrated design, with the US4B taste space as the preference backbone and the World Food Atlas as geographic grounding, outperforms generic LLM recommenders on food queries and avoids hallucinated answers.

Load-bearing premise

The preferential taste model assumes that the number of taste-related molecules in a dish's ingredients, summed from a molecule database, faithfully captures how sweet, bitter, umami, salty, sour, or spicy a person perceives the dish; this mapping is never checked against human sensory ratings.

Editorial extensions

If this is right

F-RLP replaces free-form LLM outputs with selections from a real option list, so a recommended dish is always an obtainable food rather than a hallucinated name.
Counterfactual sample engineering trains the LLM on plausible alternative dietary choices, and the thesis reports positive improvement across all tested configuration categories relative to no counterfactual generation.
The framework is the first to connect the food logger, personal model, knowledge graph, and geolocation atlas into one LLM-based pipeline, making location-aware contextual recommendations possible.
Context-aware personal taste profiles built from the US4B space predict food choices better when stress and temperature are included than when contextual factors are ignored.
Roughly 100 days of logged events are sufficient for the context-aware model to stabilize, suggesting the data collection burden is feasible for real deployment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the taste-space mapping holds up against human sensory data, the same molecule-counting recipe could be extended to other sensory dimensions, such as texture or aroma, to build preference models without relying on explicit user ratings.
The option-list constraint that guarantees valid targets gives up some serendipity; a natural test is whether adding the World Food Atlas's location-aware options reduces diversity of recommendations compared with unconstrained generation.
The counterfactual engineering step is not food-specific: any high-cardinality recommendation domain with a constrained item set could adopt the CFG retraining and option-list interface.
A consequence the thesis leaves implicit is that the World Food Atlas provides spatial grounding that lets the LLM answer 'what is available near me' without retraining, which may matter more for adoption than the taste model itself.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The genuinely new thing here is the synthesis: multimedia logging, the World Food Atlas schema, the US4B taste space, and counterfactual-generated LLM templates are wired together into one F-RLP pipeline. That integrated view is more domain-specific than generic P5-style RLP, and the engineering instinct is sound—particularly the idea of constraining LLM output to a real option list and using counterfactual sample engineering to improve training. Credit is also due to the World Food Atlas chapter, which grounds the schema in interviews with physicians and nutritionists and defines concrete query classes rather than just waving at the idea of a food atlas.

Now the soft spots. The central claim of improved food recommendation is not verifiable from the available text: Chapter 7's quantitative results are referenced and captioned but not included in the review copy. The abstract promises a feasibility study, and Figure 7.7 shows an illustrative comparison with GPT-3.5, but no numbers or baselines are visible. That alone prevents a verdict on the headline.

The deeper problem is the taste-space foundation. Section 5.5 builds each dish's US4B vector by counting taste-associated molecules in FlavorDB ingredients and summing over recipe ingredients. That equates molecular presence with perceived taste intensity, ignores dose-response and interaction effects, and is never checked against human sensory ratings. The stress-test note is right: the preferential personal vector in F-RLP is constructed on top of this mapping, so the 'truly personalized' claim inherits an unvalidated preference model. The thesis itself admits in Section 3.6 that the taste space is 'less than the tip of the proverbial iceberg,' which is honest but does not fix the load-bearing role this representation plays.

The Chapter 5 experiments are also entirely synthetic: five simulated people, a Markov-chain event generator, and parameters chosen by the authors to embody their own contextual hypotheses. Finding that context helps under those conditions is a check of the generator, not evidence about real food choice.

None of this means the work is incoherent or unserious. The author has published several components separately, and the framework is laid out carefully. But F-RLP's contribution is integration, and the evaluation has to show the whole is more than the sum. Right now it does not.

My recommendation for peer review: engage with this, but require the missing feasibility numbers and a real sensitivity or validation discussion for the US4B mapping before it is accepted. I would send it to a senior referee with a request for major revision, not desk-reject the underlying program—but I would not accept it in its current form.

Referee Report

3 major / 5 minor

Summary. The manuscript is a doctoral dissertation proposing an integrated framework, F-RLP (Food Recommendation as Language Processing), for personalized, context-aware LLM-based food recommendation. It argues that existing food recommendation systems underperform because their components (logging, personal models, knowledge graphs, geospatial data) are fragmented, and because generic LLM recommendation strategies fail to exploit food-domain structure. The thesis first develops a multimedia food logger and a World Food Atlas (WFA), then introduces a Personal Food Model (PFM) split into biological and preferential components, with the preferential side built on a six-dimensional US4B taste space. Chapters 5 and 6 present a context-aware preference model and a WFA architecture; Chapter 7 is described as an integrated feasibility study in which F-RLP connects the data, uses counterfactual sample engineering to retrain an LLM, and constrains outputs to a provided list of real options. The abstract claims that F-RLP provides 'a robust infrastructure for effective, contextual, and truly personalized food recommendations.'

Significance. If the claims were fully supported, the manuscript would make a useful contribution by laying out a modular architecture for food-domain LLM recommenders, proposing the WFA as a geospatial food data layer, and introducing counterfactual sample generation plus option-list constraints to reduce LLM hallucination. The emphasis on domain-specific personal models and the explicit treatment of location as a context input are reasonable and timely ideas. However, the evidence provided for the central claims is currently incomplete. The feasibility study results that would validate F-RLP are not present in the reviewable text, and the empirical results that are present in Chapter 5 are generated from synthetic data whose parameters encode the very effects being tested. The preference model also rests on an unvalidated molecular-counting taste mapping. These issues affect the load-bearing claims of the thesis, not merely its presentation.

major comments (3)

[Chapter 7, especially Sections 7.3–7.7 and Figure 7.4] The central empirical claim of the dissertation—that F-RLP improves food recommendation over generic RLP and over its own No-CFG baseline—is not verifiable from the provided text. Section 7.7 is listed in the table of contents, but the actual quantitative results are not included; Figure 7.4's caption asserts 'positive enhancement across all categories' without reporting the underlying values, baselines, error bars, or significance tests. Please supply the full feasibility-study results, including a comparison with the No-CFG configuration, a generic RLP baseline (e.g., GPT-3.5), and standard recommendation metrics (e.g., NDCG, Recall@K), together with the experimental setup needed for reproducibility. Without these numbers, the central contribution is asserted rather than demonstrated.
[Sections 5.5, 5.7, and 5.8] The context-aware preference model is validated only on synthetic data generated by a Markov-chain model whose parameters were chosen by the authors to encode the same contextual effects on taste that the event-mining pipeline is then evaluated on (Section 5.7). Predicting the planted relationships in RQ1–RQ3 (Sections 5.8.1–5.8.3) demonstrates internal consistency of the generator, not that the model captures real users' contextual taste variation. The statement in Section 5.8.2 that 'adding contextual information leads to a better performance' is therefore unsupported as evidence about actual food preferences. Real user data or an externally grounded validation set is needed before the model can be claimed to improve preference prediction.
[Sections 3.6, 5.5, and 7.2] The 'truly personalized' claim depends on the US4B taste space, which is constructed in Section 5.5 by counting taste-related molecules per ingredient in FlavorDB and summing these counts over the dish's ingredients. This mapping assumes molecular presence is proportional to perceived taste intensity, although the paper itself notes that FlavorDB contains no intensity information and calls the taste space 'less than the tip of the proverbial iceberg' in Section 3.6. No validation against human sensory ratings is provided. Since the preferential personal vector used in F-RLP (Section 7.2) is derived from these taste profiles, the personalization claim inherits this unvalidated assumption. Please either validate the taste mapping against human sensory data or temper the 'truly personalized' wording to match what is actually demonstrated.

minor comments (5)

[Throughout (e.g., Sections 1.3 and 7.4)] The phrase 'prove of concept' appears repeatedly; it should be 'proof of concept'.
[Sections 4.2.2 and 4.2.4] Section 4.2.4 (Food Journal History) largely repeats the description already given in Section 4.2.2, and the in-text reference to 'figure 2' is ambiguous; please use consistent figure numbering and remove duplicate text.
[Section 5.8.1] Only User1 and User5 are discussed in the radar-plot analysis; the remaining three users' contextual patterns are not described, so it is unclear whether the reported temperature and stress effects generalize across the synthetic population.
[Section 3.2.2] The taste space is referred to as both 'US4B' and 'USSSSB'; please define the acronym once and use a single consistent term throughout.
[Section 6.2.2] The text is truncated mid-sentence in the version provided for review ('What di ...'); please ensure the final manuscript contains the complete query examples and the full algorithm listings.

Circularity Check

2 steps flagged · score 6.0 of 10

Chapter 5's synthetic-data experiment encodes the contextual hypotheses it then reports as validated, and the F-RLP personal vector rests on the unvalidated US4B taste space imported from the author's own prior paper.

fitted input called prediction [§5.7 Experimental Design; §5.8.1–§5.8.2 Results]
"We opted to utilize synthesized data for the experiments because we can use the ground truth of contextual factors’ impact on taste to validate the model ... We designed the parameters associated with the stress-related causal aspect of a food choice based on the available findings such that if a person had a stressful day, it would impact their food choice towards more palatable foods for some subjects and towards less appetite for others. ... As we expected, adding contextual information leads to a better performance than the ’No-Context’ model for all five individuals in our dataset."

The Markov-chain event generator's transition parameters were set by the authors to encode exactly the contextual dependencies (stress increases palatable-food choice; weather alters food choice) that the event-mining experiments then 'discover' and report as evidence. The 'ground truth' used to validate the model is therefore not external; it is the same set of design choices used to synthesize the data. RQ1's contextual taste-profile variation and RQ2's context-aware accuracy gain are consequences of the generator's construction, so the experimental confirmation is forced by construction rather than by independent data.
ansatz smuggled in via citation [§5.5 Food Preference Space (Taste space); used in §7.2 Personal Vector Generation]
"In [172], the authors demonstrated how a unified and robust taste space model is required to create a preferential personal food model. They presented the US4B taste space, which includes six dimensions: umami, salty, sweet, sour, spicy, and bitter. However, that work was the initial step and did not provide any actual taste dataset or concrete approach to build such a dataset."

The six-dimensional US4B taste representation is not an externally established fact; it is imported from the author's own earlier paper [172], and the thesis itself concedes that this earlier work provided no dataset or concrete method to compute US4B values. The preferential personal vector in F-RLP (§7.2) is built on this taste space, so the claimed 'truly personalized' recommendations inherit an unvalidated ansatz. The self-citation does not add independent evidence; it merely reintroduces the same proposal that the thesis then treats as a foundation.

full rationale

The most defensible circularities are the two above. The remaining self-citations (food logger, World Food Atlas, event mining) describe system components and are not, by themselves, derivations that reduce to their inputs. The taste-molecule counting in §5.5 is an unvalidated approximation—a correctness risk—but not a circular step, since the paper does not claim to derive perceived taste from molecule counts in a way that is then used to prove the counting method. The CFG algorithm in Fig. 7.3 deterministically selects the option that scores highest on its own priority metrics; if the 'comparison metrics' in Fig. 7.4 are those same priority metrics, the reported improvement is also by construction, but the text is too sparse to score that as a separate formal step. Overall, because a central experimental validation (context-aware preference modeling) is produced by the same assumptions it claims to test, and because the personalization foundation (US4B) is a self-cited ansatz, the score is 6 rather than 0–2.

Assumptions & free parameters 3 free parameters · 4 assumptions · 3 invented entities

The central claim rests on several hand-chosen constructs and domain assumptions. The US4B taste space and the molecule-counting mapping are invented without external validation. The evaluation of the context model depends on synthetic data generated from assumptions that mirror the authors' hypotheses. The WFA and F-RLP are proposed but not deployed or externally benchmarked. These are not free parameters in the usual fitting sense, but they are modeling choices that the reader must accept without independent evidence.

free parameters (3)

US4B taste dimensions
The six tastes (umami, salty, sweet, sour, spicy, bitter) are chosen as the basis for the taste space; this is a modeling assumption, not derived from data.
Markov-chain event generation parameters = not specified
The synthetic event stream uses probabilities and context effects (stress, temperature) 'carefully selected' to imitate real data; values are not reported in the visible text.
Temporal windows for personal vectors = 1 day, 3 days, 3 months, 12 months
Chosen by hand to balance recency and history in the biological and preferential vectors; no justification from data or literature is given.

assumptions (4)

domain assumption US4B taste space captures the relevant dimensions of taste
Section 3.2.2 introduces US4B as analogous to RGB for color; no empirical validation is provided.
domain assumption Taste molecules from FlavorDB can be mapped by counting to ingredient taste vectors
Section 5.5 describes this mapping; the intensity of tastes is ignored due to dataset limitations, yet vectors are used additively for dishes.
domain assumption Synthetic Markov-chain data resembles real user food events
Section 5.7 states parameters are 'carefully selected' to make events resemble real data; validity of this assumption underlies all Chapter 5 results.
domain assumption Event mining with the potential outcomes framework yields valid causal relationships
Section 3.5 states causal inference requires assumptions such as SUTVA to be valid; these are not tested on the data.

invented entities (3)

World Food Atlas (WFA)
purpose: A geospatial database for food queries based on location; enables location-aware recommendations.
The thesis describes a schema and architecture but no deployed system or public data source; no falsifiable prediction is made outside the paper.
US4B taste space
purpose: A six-dimensional representation of taste for estimating dish taste profiles from ingredients.
No empirical validation or external benchmark is provided; the taste vectors are computed from a counting heuristic and are used to generate the very data used in evaluation.
F-RLP framework
purpose: A food-specific LLM recommendation paradigm with counterfactual generation and option-list restriction.
Only a proof-of-concept is described; results not visible in the available text; no public code or reproducible evaluation is provided.

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Cite this review

Pith. "Pith review of An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation." pith.science (2026). https://pith.science/paper/UFZSLCQZ

@misc{pith2026250420092,
  author       = {Pith},
  title        = {Pith review of: An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UFZSLCQZ}},
  note         = {Machine review of arXiv:2504.20092}
}

read the original abstract

Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.

Figures

Figures reproduced from arXiv: 2504.20092 by the authors.

**Figure 1.1.** Structure of the thesis The subsequent sections of this dissertation, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p023_1_1.png] view at source ↗

**Figure 2.1.** The food computing layers and the pyramid of the body of work. A simple [PITH_FULL_IMAGE:figures/full_fig_p027_2_1.png] view at source ↗

**Figure 2.2.** The interaction between the different layers of food computing shows that these [PITH_FULL_IMAGE:figures/full_fig_p029_2_2.png] view at source ↗

Figures from the paper (38 more)

**Figure 2.3.** Figure 2.3: Overview of the Contextual Personal Food Recommendation Components. The [PITH_FULL_IMAGE:figures/full_fig_p050_2_3.png]

**Figure 2.4.** Figure 2.4: Food logging will use multimedia input sources and complement information [PITH_FULL_IMAGE:figures/full_fig_p051_2_4.png]

**Figure 2.5.** Figure 2.5: Food Knowledge Graph aggregating and unifying food-related information from [PITH_FULL_IMAGE:figures/full_fig_p052_2_5.png]

**Figure 2.6.** Figure 2.6: In what follows in this thesis we explore this proposed holistic architecture. [PITH_FULL_IMAGE:figures/full_fig_p056_2_6.png]

**Figure 3.1.** Figure 3.1: Food Recommendation Architecture: Data from the 3 digestion phases are being [PITH_FULL_IMAGE:figures/full_fig_p061_3_1.png]

**Figure 3.** Figure 3: illustrates the process through which the personicle aggregates various data [PITH_FULL_IMAGE:figures/full_fig_p062_3.png]

**Figure 3.2.** Figure 3.2: The interactions in the biological food model [PITH_FULL_IMAGE:figures/full_fig_p062_3_2.png]

**Figure 3.3.** Figure 3.3: Visualization of the US4B Taste Space. Part A: The collection of all taste [PITH_FULL_IMAGE:figures/full_fig_p063_3_3.png]

**Figure 3.4.** Figure 3.4: Food logging will use multimedia input sources and complement information [PITH_FULL_IMAGE:figures/full_fig_p067_3_4.png]

**Figure 3.5.** Figure 3.5: Attributes of food events that impact sleep quality. These relationships form the [PITH_FULL_IMAGE:figures/full_fig_p070_3_5.png]

**Figure 3.6.** Figure 3.6: Event Mining workflow: Hypothesis generation operators allow us to find fre [PITH_FULL_IMAGE:figures/full_fig_p071_3_6.png]

**Figure 3.** Figure 3: shows the complete event mining process for the personal food model. The verified [PITH_FULL_IMAGE:figures/full_fig_p074_3.png]

**Figure 4.1.** Figure 4.1: Food logger uses multimedia input sources and uses online sources to add addi [PITH_FULL_IMAGE:figures/full_fig_p086_4_1.png]

**Figure 4.2.** Figure 4.2: Food logger app architecture. 69 [PITH_FULL_IMAGE:figures/full_fig_p086_4_2.png]

**Figure 4.3.** Figure 4.3: Food Knowledge Graph representation in the Neo4j environment. B1136 is the [PITH_FULL_IMAGE:figures/full_fig_p092_4_3.png]

**Figure 5.1.** Figure 5.1: Personal food computing overview and relevant proposed layers. [PITH_FULL_IMAGE:figures/full_fig_p095_5_1.png]

**Figure 5.2.** Figure 5.2: Food event causal analysis. 5.3 Food Event Model A single food event has multiple facets captured by different applications. The food being eaten, the time of the day, the amount of food, the location type, the ambience, the person 81 [PITH_FULL_IMAGE:figures/full_f…

**Figure 5.3.** Figure 5.3: Taste Space Generation: This figure illustrates our approach to map the food [PITH_FULL_IMAGE:figures/full_fig_p101_5_3.png]

**Figure 5.4.** Figure 5.4: Causal Preferential Model Architecture: The food logging platform captures the [PITH_FULL_IMAGE:figures/full_fig_p102_5_4.png]

**Figure 5.5.** Figure 5.5: Dataset Summary: This figure displays a summary of our events dataset. This [PITH_FULL_IMAGE:figures/full_fig_p105_5_5.png]

**Figure 5.6.** Figure 5.6: Experimental Design: This figure illustrates how we perform hypothesis testing [PITH_FULL_IMAGE:figures/full_fig_p107_5_6.png]

**Figure 5.** Figure 5: shows the variation in the preferred taste profile with different contextual variables [PITH_FULL_IMAGE:figures/full_fig_p108_5.png]

**Figure 5.7.** Figure 5.7: Variation in taste preferences with context. This figure shows how the preferences [PITH_FULL_IMAGE:figures/full_fig_p109_5_7.png]

**Figure 5.8.** Figure 5.8: Model performance using Top-5 predictions accuracy. We can see that for all [PITH_FULL_IMAGE:figures/full_fig_p110_5_8.png]

**Figure 5.9.** Figure 5.9: Model accuracy vs training data volume. With adequate volume, the model 96 [PITH_FULL_IMAGE:figures/full_fig_p113_5_9.png]

**Figure 6.1.** Figure 6.1: World Food Atlas for Food Recommendation [PITH_FULL_IMAGE:figures/full_fig_p117_6_1.png]

**Figure 6.2.** Figure 6.2: World Food Atlas Architecture: The localized food data is posted onto WFA via [PITH_FULL_IMAGE:figures/full_fig_p120_6_2.png]

**Figure 6.3.** Figure 6.3: Health-related queries encompass questions that affect the user’s biology. We [PITH_FULL_IMAGE:figures/full_fig_p122_6_3.png]

**Figure 6.4.** Figure 6.4: World Food Atlas schema Design for processing real-world queries [PITH_FULL_IMAGE:figures/full_fig_p124_6_4.png]

**Figure 7.1.** Figure 7.1: RLP vs F-RLP: On the left side we see a general propose LLM-based recommen [PITH_FULL_IMAGE:figures/full_fig_p136_7_1.png]

**Figure 7.2.** Figure 7.2: This figure illustrates the dual-component structure of personal vectors in food [PITH_FULL_IMAGE:figures/full_fig_p138_7_2.png]

**Figure 7.3.** Figure 7.3: The algorithm for the counterfactual generation: This algorithm first sorts the [PITH_FULL_IMAGE:figures/full_fig_p143_7_3.png]

**Figure 7.4.** Figure 7.4: For each category, we utilize comparison metrics to assess the degree of improve [PITH_FULL_IMAGE:figures/full_fig_p145_7_4.png]

**Figure 7.5.** Figure 7.5: Showcasing a demo of template-based sample recommendation sample generation [PITH_FULL_IMAGE:figures/full_fig_p148_7_5.png]

**Figure 7.6.** Figure 7.6: An exemplar of final result when training using the the ID representation of [PITH_FULL_IMAGE:figures/full_fig_p149_7_6.png]

**Figure 7.7.** Figure 7.7: Showcasing F-RLP’s meaningful and precise answer comparison with GPT 3.5 [PITH_FULL_IMAGE:figures/full_fig_p150_7_7.png]

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